Comparación de fotos de los tipos de fracturas

# libreria de lectura de bitmaps para bmp, jpeg, png y tiff
library(readbitmap)
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library(fda)
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## Loading required package: splines
## Loading required package: fds
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library(ggplot2)
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library(dplyr)
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library(caret)
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1. Cargar imagenes dĆŗctiles

Total de imagenes 10. Por cada foto 400 datos funcionales (lineas de pixeles en X y lĆ­neas de pixeles en y), para un total de 4000 datos.

imagenductilent1 <- read.bitmap("DuctilEnt1.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilent1 <- imagenductilent1 [,,1] # Deja un solo canal
imagenductilent1t <- t(imagenductilent1) 
imagenductilent1xey <- rbind(imagenductilent1,imagenductilent1t)
imagenductilent1xey <- t(imagenductilent1xey)
dim(imagenductilent1xey)
## [1] 200 400
imagenductilent2 <- read.bitmap("DuctilEnt2.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilent2 <- imagenductilent2 [,,1] # Deja un solo canal
imagenductilent2t <- t(imagenductilent2) 
imagenductilent2xey <- rbind(imagenductilent2,imagenductilent2t)
imagenductilent2xey <- t(imagenductilent2xey)
dim(imagenductilent2xey)
## [1] 200 400
imagenductilent3 <- read.bitmap("DuctilEnt3.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilent3 <- imagenductilent3 [,,1] # Deja un solo canal
imagenductilent3t <- t(imagenductilent3) 
imagenductilent3xey <- rbind(imagenductilent3,imagenductilent3t)
imagenductilent3xey <- t(imagenductilent3xey)
dim(imagenductilent3xey)
## [1] 200 400
imagenductilent4 <- read.bitmap("DuctilEnt4.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilent4 <- imagenductilent4 [,,1] # Deja un solo canal
imagenductilent4t <- t(imagenductilent4) 
imagenductilent4xey <- rbind(imagenductilent4,imagenductilent4t)
imagenductilent4xey <- t(imagenductilent4xey)
dim(imagenductilent4xey)
## [1] 200 400
imagenductilent5 <- read.bitmap("DuctilEnt5.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilent5 <- imagenductilent5 [,,1] # Deja un solo canal
imagenductilent5t <- t(imagenductilent5) 
imagenductilent5xey <- rbind(imagenductilent5,imagenductilent5t)
imagenductilent5xey <- t(imagenductilent5xey)
dim(imagenductilent5xey)
## [1] 200 400
imagenductilent6 <- read.bitmap("DuctilEnt6.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilent6 <- imagenductilent6 [,,1] # Deja un solo canal
imagenductilent6t <- t(imagenductilent6) 
imagenductilent6xey <- rbind(imagenductilent6,imagenductilent6t)
imagenductilent6xey <- t(imagenductilent6xey)
dim(imagenductilent6xey)
## [1] 200 400
imagenductilent7 <- read.bitmap("DuctilEnt7.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilent7 <- imagenductilent7 [,,1] # Deja un solo canal
imagenductilent7t <- t(imagenductilent7) 
imagenductilent7xey <- rbind(imagenductilent7,imagenductilent7t)
imagenductilent7xey <- t(imagenductilent7xey)
dim(imagenductilent7xey)
## [1] 200 400
imagenductilens1 <- read.bitmap("DuctilEns1.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilens1 <- imagenductilens1 [,,1] # Deja un solo canal
imagenductilens1t <- t(imagenductilens1) 
imagenductilens1xey <- rbind(imagenductilens1,imagenductilens1t)
imagenductilens1xey <- t(imagenductilens1xey)
dim(imagenductilens1xey)
## [1] 200 400
imagenductilens2 <- read.bitmap("DuctilEns2.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilens2 <- imagenductilens2 [,,1] # Deja un solo canal
imagenductilens2t <- t(imagenductilens2) 
imagenductilens2xey <- rbind(imagenductilens2,imagenductilens2t)
imagenductilens2xey <- t(imagenductilens2xey)
dim(imagenductilens2xey)
## [1] 200 400
imagenductilens3 <- read.bitmap("DuctilEns3.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenductilens3 <- imagenductilens3 [,,1] # Deja un solo canal
imagenductilens3t <- t(imagenductilens3) 
imagenductilens3xey <- rbind(imagenductilens3,imagenductilens3t)
imagenductilens3xey <- t(imagenductilens3xey)
dim(imagenductilens3xey)
## [1] 200 400
imagenductilxey <- cbind(imagenductilent1xey,imagenductilent2xey,imagenductilent3xey,imagenductilent4xey,imagenductilent5xey,imagenductilent6xey,imagenductilent7xey,imagenductilens1xey,imagenductilens2xey,imagenductilens3xey)

dim(imagenductilxey)
## [1]  200 4000

2. Cargar imagenes frƔgiles

Total de imagenes 10. Por cada foto 400 datos funcionales (lineas de pixeles en X y lĆ­neas de pixeles en y), para un total de 4000 datos.

imagenfragilent1 <- read.bitmap("FragilEnt1.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilent1 <- imagenfragilent1 [,,1] # Deja un solo canal
imagenfragilent1t <- t(imagenfragilent1) 
imagenfragilent1xey <- rbind(imagenfragilent1,imagenfragilent1t)
imagenfragilent1xey <- t(imagenfragilent1xey)
dim(imagenfragilent1xey)
## [1] 200 400
imagenfragilent2 <- read.bitmap("FragilEnt2.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilent2 <- imagenfragilent2 [,,1] # Deja un solo canal
imagenfragilent2t <- t(imagenfragilent2) 
imagenfragilent2xey <- rbind(imagenfragilent2,imagenfragilent2t)
imagenfragilent2xey <- t(imagenfragilent2xey)
dim(imagenfragilent2xey)
## [1] 200 400
imagenfragilent3 <- read.bitmap("FragilEnt3.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilent3 <- imagenfragilent3 [,,1] # Deja un solo canal
imagenfragilent3t <- t(imagenfragilent3) 
imagenfragilent3xey <- rbind(imagenfragilent3,imagenfragilent3t)
imagenfragilent3xey <- t(imagenfragilent3xey)
dim(imagenfragilent3xey)
## [1] 200 400
imagenfragilent4 <- read.bitmap("FragilEnt4.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilent4 <- imagenfragilent4 [,,1] # Deja un solo canal
imagenfragilent4t <- t(imagenfragilent4) 
imagenfragilent4xey <- rbind(imagenfragilent4,imagenfragilent4t)
imagenfragilent4xey <- t(imagenfragilent4xey)
dim(imagenfragilent4xey)
## [1] 200 400
imagenfragilent5 <- read.bitmap("FragilEnt5.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilent5 <- imagenfragilent5 [,,1] # Deja un solo canal
imagenfragilent5t <- t(imagenfragilent5) 
imagenfragilent5xey <- rbind(imagenfragilent5,imagenfragilent5t)
imagenfragilent5xey <- t(imagenfragilent5xey)
dim(imagenfragilent5xey)
## [1] 200 400
imagenfragilent6 <- read.bitmap("FragilEnt6.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilent6 <- imagenfragilent6 [,,1] # Deja un solo canal
imagenfragilent6t <- t(imagenfragilent6) 
imagenfragilent6xey <- rbind(imagenfragilent6,imagenfragilent6t)
imagenfragilent6xey <- t(imagenfragilent6xey)
dim(imagenfragilent6xey)
## [1] 200 400
imagenfragilent7 <- read.bitmap("FragilEnt7.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilent7 <- imagenfragilent7 [,,1] # Deja un solo canal
imagenfragilent7t <- t(imagenfragilent7) 
imagenfragilent7xey <- rbind(imagenfragilent7,imagenfragilent7t)
imagenfragilent7xey <- t(imagenfragilent7xey)
dim(imagenfragilent7xey)
## [1] 200 400
imagenfragilens1 <- read.bitmap("FragilEns1.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilens1 <- imagenfragilens1 [,,1] # Deja un solo canal
imagenfragilens1t <- t(imagenfragilens1) 
imagenfragilens1xey <- rbind(imagenfragilens1,imagenfragilens1t)
imagenfragilens1xey <- t(imagenfragilens1xey)
dim(imagenfragilens1xey)
## [1] 200 400
imagenfragilens2 <- read.bitmap("FragilEns2.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilens2 <- imagenfragilens2 [,,1] # Deja un solo canal
imagenfragilens2t <- t(imagenfragilens2) 
imagenfragilens2xey <- rbind(imagenfragilens2,imagenfragilens2t)
imagenfragilens2xey <- t(imagenfragilens2xey)
dim(imagenfragilens2xey)
## [1] 200 400
imagenfragilens3 <- read.bitmap("fragilEns3.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfragilens3 <- imagenfragilens3 [,,1] # Deja un solo canal
imagenfragilens3t <- t(imagenfragilens3) 
imagenfragilens3xey <- rbind(imagenfragilens3,imagenfragilens3t)
imagenfragilens3xey <- t(imagenfragilens3xey)
dim(imagenfragilens3xey)
## [1] 200 400
imagenfragilxey <- cbind(imagenfragilent1xey,imagenfragilent2xey,imagenfragilent3xey,imagenfragilent4xey,imagenfragilent5xey,imagenfragilent6xey,imagenfragilent7xey,imagenfragilens1xey,imagenfragilens2xey,imagenfragilens3xey)

dim(imagenfragilxey)
## [1]  200 4000

3. Cargar imagenes fatiga

Total de imagenes 10. Por cada foto 400 datos funcionales (lineas de pixeles en X y lĆ­neas de pixeles en y), para un total de 4000 datos.

imagenfatigaent1 <- read.bitmap("FatigaEnt1.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaent1 <- imagenfatigaent1 [,,1] # Deja un solo canal
imagenfatigaent1t <- t(imagenfatigaent1) 
imagenfatigaent1xey <- rbind(imagenfatigaent1,imagenfatigaent1t)
imagenfatigaent1xey <- t(imagenfatigaent1xey)
dim(imagenfatigaent1xey)
## [1] 200 400
imagenfatigaent2 <- read.bitmap("FatigaEnt2.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaent2 <- imagenfatigaent2 [,,1] # Deja un solo canal
imagenfatigaent2t <- t(imagenfatigaent2) 
imagenfatigaent2xey <- rbind(imagenfatigaent2,imagenfatigaent2t)
imagenfatigaent2xey <- t(imagenfatigaent2xey)
dim(imagenfatigaent2xey)
## [1] 200 400
imagenfatigaent3 <- read.bitmap("FatigaEnt3.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaent3 <- imagenfatigaent3 [,,1] # Deja un solo canal
imagenfatigaent3t <- t(imagenfatigaent3) 
imagenfatigaent3xey <- rbind(imagenfatigaent3,imagenfatigaent3t)
imagenfatigaent3xey <- t(imagenfatigaent3xey)
dim(imagenfatigaent3xey)
## [1] 200 400
imagenfatigaent4 <- read.bitmap("FatigaEnt4.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaent4 <- imagenfatigaent4 [,,1] # Deja un solo canal
imagenfatigaent4t <- t(imagenfatigaent4) 
imagenfatigaent4xey <- rbind(imagenfatigaent4,imagenfatigaent4t)
imagenfatigaent4xey <- t(imagenfatigaent4xey)
dim(imagenfatigaent4xey)
## [1] 200 400
imagenfatigaent5 <- read.bitmap("FatigaEnt5.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaent5 <- imagenfatigaent5 [,,1] # Deja un solo canal
imagenfatigaent5t <- t(imagenfatigaent5) 
imagenfatigaent5xey <- rbind(imagenfatigaent5,imagenfatigaent5t)
imagenfatigaent5xey <- t(imagenfatigaent5xey)
dim(imagenfatigaent5xey)
## [1] 200 400
imagenfatigaent6 <- read.bitmap("FatigaEnt6.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaent6 <- imagenfatigaent6 [,,1] # Deja un solo canal
imagenfatigaent6t <- t(imagenfatigaent6) 
imagenfatigaent6xey <- rbind(imagenfatigaent6,imagenfatigaent6t)
imagenfatigaent6xey <- t(imagenfatigaent6xey)
dim(imagenfatigaent6xey)
## [1] 200 400
imagenfatigaent7 <- read.bitmap("FatigaEnt7.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaent7 <- imagenfatigaent7 [,,1] # Deja un solo canal
imagenfatigaent7t <- t(imagenfatigaent7) 
imagenfatigaent7xey <- rbind(imagenfatigaent7,imagenfatigaent7t)
imagenfatigaent7xey <- t(imagenfatigaent7xey)
dim(imagenfatigaent7xey)
## [1] 200 400
imagenfatigaens1 <- read.bitmap("FatigaEns1.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaens1 <- imagenfatigaens1 [,,1] # Deja un solo canal
imagenfatigaens1t <- t(imagenfatigaens1) 
imagenfatigaens1xey <- rbind(imagenfatigaens1,imagenfatigaens1t)
imagenfatigaens1xey <- t(imagenfatigaens1xey)
dim(imagenfatigaens1xey)
## [1] 200 400
imagenfatigaens2 <- read.bitmap("FatigaEns2.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaens2 <- imagenfatigaens2 [,,1] # Deja un solo canal
imagenfatigaens2t <- t(imagenfatigaens2) 
imagenfatigaens2xey <- rbind(imagenfatigaens2,imagenfatigaens2t)
imagenfatigaens2xey <- t(imagenfatigaens2xey)
dim(imagenfatigaens2xey)
## [1] 200 400
imagenfatigaens3 <- read.bitmap("FatigaEns3.jpg") # crea una matriz de x filas y columanas y z=3 canales
imagenfatigaens3 <- imagenfatigaens3 [,,1] # Deja un solo canal
imagenfatigaens3t <- t(imagenfatigaens3) 
imagenfatigaens3xey <- rbind(imagenfatigaens3,imagenfatigaens3t)
imagenfatigaens3xey <- t(imagenfatigaens3xey)
dim(imagenfatigaens3xey)
## [1] 200 400
imagenfatigaxey <- cbind(imagenfatigaent1xey,imagenfatigaent2xey,imagenfatigaent3xey,imagenfatigaent4xey,imagenfatigaent5xey,imagenfatigaent6xey,imagenfatigaent7xey,imagenfatigaens1xey,imagenfatigaens2xey,imagenfatigaens3xey)

dim(imagenfatigaxey)
## [1]  200 4000

4 AnƔlisis de Componentes Principales de Todas las Curvas en Conjunto

El objetivo es verificar si se forman clusteres por tipo de curvas.

4.1 Suavizamiento conjunto de curvas dúctil, frÔgil y fatiga

imagenductilfragilfatigaxey <- cbind(imagenductilxey,imagenfragilxey,imagenfatigaxey)
# Convertir en Data Frame
imagenductilfragilfatigaxeydf <- as.data.frame (imagenductilfragilfatigaxey)
attributes(imagenductilfragilfatigaxeydf)
## $names
##     [1] "V1"     "V2"     "V3"     "V4"     "V5"     "V6"     "V7"     "V8"    
##     [9] "V9"     "V10"    "V11"    "V12"    "V13"    "V14"    "V15"    "V16"   
##    [17] "V17"    "V18"    "V19"    "V20"    "V21"    "V22"    "V23"    "V24"   
##    [25] "V25"    "V26"    "V27"    "V28"    "V29"    "V30"    "V31"    "V32"   
##    [33] "V33"    "V34"    "V35"    "V36"    "V37"    "V38"    "V39"    "V40"   
##    [41] "V41"    "V42"    "V43"    "V44"    "V45"    "V46"    "V47"    "V48"   
##    [49] "V49"    "V50"    "V51"    "V52"    "V53"    "V54"    "V55"    "V56"   
##    [57] "V57"    "V58"    "V59"    "V60"    "V61"    "V62"    "V63"    "V64"   
##    [65] "V65"    "V66"    "V67"    "V68"    "V69"    "V70"    "V71"    "V72"   
##    [73] "V73"    "V74"    "V75"    "V76"    "V77"    "V78"    "V79"    "V80"   
##    [81] "V81"    "V82"    "V83"    "V84"    "V85"    "V86"    "V87"    "V88"   
##    [89] "V89"    "V90"    "V91"    "V92"    "V93"    "V94"    "V95"    "V96"   
##    [97] "V97"    "V98"    "V99"    "V100"   "V101"   "V102"   "V103"   "V104"  
##   [105] "V105"   "V106"   "V107"   "V108"   "V109"   "V110"   "V111"   "V112"  
##   [113] "V113"   "V114"   "V115"   "V116"   "V117"   "V118"   "V119"   "V120"  
##   [121] "V121"   "V122"   "V123"   "V124"   "V125"   "V126"   "V127"   "V128"  
##   [129] "V129"   "V130"   "V131"   "V132"   "V133"   "V134"   "V135"   "V136"  
##   [137] "V137"   "V138"   "V139"   "V140"   "V141"   "V142"   "V143"   "V144"  
##   [145] "V145"   "V146"   "V147"   "V148"   "V149"   "V150"   "V151"   "V152"  
##   [153] "V153"   "V154"   "V155"   "V156"   "V157"   "V158"   "V159"   "V160"  
##   [161] "V161"   "V162"   "V163"   "V164"   "V165"   "V166"   "V167"   "V168"  
##   [169] "V169"   "V170"   "V171"   "V172"   "V173"   "V174"   "V175"   "V176"  
##   [177] "V177"   "V178"   "V179"   "V180"   "V181"   "V182"   "V183"   "V184"  
##   [185] "V185"   "V186"   "V187"   "V188"   "V189"   "V190"   "V191"   "V192"  
##   [193] "V193"   "V194"   "V195"   "V196"   "V197"   "V198"   "V199"   "V200"  
##   [201] "V201"   "V202"   "V203"   "V204"   "V205"   "V206"   "V207"   "V208"  
##   [209] "V209"   "V210"   "V211"   "V212"   "V213"   "V214"   "V215"   "V216"  
##   [217] "V217"   "V218"   "V219"   "V220"   "V221"   "V222"   "V223"   "V224"  
##   [225] "V225"   "V226"   "V227"   "V228"   "V229"   "V230"   "V231"   "V232"  
##   [233] "V233"   "V234"   "V235"   "V236"   "V237"   "V238"   "V239"   "V240"  
##   [241] "V241"   "V242"   "V243"   "V244"   "V245"   "V246"   "V247"   "V248"  
##   [249] "V249"   "V250"   "V251"   "V252"   "V253"   "V254"   "V255"   "V256"  
##   [257] "V257"   "V258"   "V259"   "V260"   "V261"   "V262"   "V263"   "V264"  
##   [265] "V265"   "V266"   "V267"   "V268"   "V269"   "V270"   "V271"   "V272"  
##   [273] "V273"   "V274"   "V275"   "V276"   "V277"   "V278"   "V279"   "V280"  
##   [281] "V281"   "V282"   "V283"   "V284"   "V285"   "V286"   "V287"   "V288"  
##   [289] "V289"   "V290"   "V291"   "V292"   "V293"   "V294"   "V295"   "V296"  
##   [297] "V297"   "V298"   "V299"   "V300"   "V301"   "V302"   "V303"   "V304"  
##   [305] "V305"   "V306"   "V307"   "V308"   "V309"   "V310"   "V311"   "V312"  
##   [313] "V313"   "V314"   "V315"   "V316"   "V317"   "V318"   "V319"   "V320"  
##   [321] "V321"   "V322"   "V323"   "V324"   "V325"   "V326"   "V327"   "V328"  
##   [329] "V329"   "V330"   "V331"   "V332"   "V333"   "V334"   "V335"   "V336"  
##   [337] "V337"   "V338"   "V339"   "V340"   "V341"   "V342"   "V343"   "V344"  
##   [345] "V345"   "V346"   "V347"   "V348"   "V349"   "V350"   "V351"   "V352"  
##   [353] "V353"   "V354"   "V355"   "V356"   "V357"   "V358"   "V359"   "V360"  
##   [361] "V361"   "V362"   "V363"   "V364"   "V365"   "V366"   "V367"   "V368"  
##   [369] "V369"   "V370"   "V371"   "V372"   "V373"   "V374"   "V375"   "V376"  
##   [377] "V377"   "V378"   "V379"   "V380"   "V381"   "V382"   "V383"   "V384"  
##   [385] "V385"   "V386"   "V387"   "V388"   "V389"   "V390"   "V391"   "V392"  
##   [393] "V393"   "V394"   "V395"   "V396"   "V397"   "V398"   "V399"   "V400"  
##   [401] "V401"   "V402"   "V403"   "V404"   "V405"   "V406"   "V407"   "V408"  
##   [409] "V409"   "V410"   "V411"   "V412"   "V413"   "V414"   "V415"   "V416"  
##   [417] "V417"   "V418"   "V419"   "V420"   "V421"   "V422"   "V423"   "V424"  
##   [425] "V425"   "V426"   "V427"   "V428"   "V429"   "V430"   "V431"   "V432"  
##   [433] "V433"   "V434"   "V435"   "V436"   "V437"   "V438"   "V439"   "V440"  
##   [441] "V441"   "V442"   "V443"   "V444"   "V445"   "V446"   "V447"   "V448"  
##   [449] "V449"   "V450"   "V451"   "V452"   "V453"   "V454"   "V455"   "V456"  
##   [457] "V457"   "V458"   "V459"   "V460"   "V461"   "V462"   "V463"   "V464"  
##   [465] "V465"   "V466"   "V467"   "V468"   "V469"   "V470"   "V471"   "V472"  
##   [473] "V473"   "V474"   "V475"   "V476"   "V477"   "V478"   "V479"   "V480"  
##   [481] "V481"   "V482"   "V483"   "V484"   "V485"   "V486"   "V487"   "V488"  
##   [489] "V489"   "V490"   "V491"   "V492"   "V493"   "V494"   "V495"   "V496"  
##   [497] "V497"   "V498"   "V499"   "V500"   "V501"   "V502"   "V503"   "V504"  
##   [505] "V505"   "V506"   "V507"   "V508"   "V509"   "V510"   "V511"   "V512"  
##   [513] "V513"   "V514"   "V515"   "V516"   "V517"   "V518"   "V519"   "V520"  
##   [521] "V521"   "V522"   "V523"   "V524"   "V525"   "V526"   "V527"   "V528"  
##   [529] "V529"   "V530"   "V531"   "V532"   "V533"   "V534"   "V535"   "V536"  
##   [537] "V537"   "V538"   "V539"   "V540"   "V541"   "V542"   "V543"   "V544"  
##   [545] "V545"   "V546"   "V547"   "V548"   "V549"   "V550"   "V551"   "V552"  
##   [553] "V553"   "V554"   "V555"   "V556"   "V557"   "V558"   "V559"   "V560"  
##   [561] "V561"   "V562"   "V563"   "V564"   "V565"   "V566"   "V567"   "V568"  
##   [569] "V569"   "V570"   "V571"   "V572"   "V573"   "V574"   "V575"   "V576"  
##   [577] "V577"   "V578"   "V579"   "V580"   "V581"   "V582"   "V583"   "V584"  
##   [585] "V585"   "V586"   "V587"   "V588"   "V589"   "V590"   "V591"   "V592"  
##   [593] "V593"   "V594"   "V595"   "V596"   "V597"   "V598"   "V599"   "V600"  
##   [601] "V601"   "V602"   "V603"   "V604"   "V605"   "V606"   "V607"   "V608"  
##   [609] "V609"   "V610"   "V611"   "V612"   "V613"   "V614"   "V615"   "V616"  
##   [617] "V617"   "V618"   "V619"   "V620"   "V621"   "V622"   "V623"   "V624"  
##   [625] "V625"   "V626"   "V627"   "V628"   "V629"   "V630"   "V631"   "V632"  
##   [633] "V633"   "V634"   "V635"   "V636"   "V637"   "V638"   "V639"   "V640"  
##   [641] "V641"   "V642"   "V643"   "V644"   "V645"   "V646"   "V647"   "V648"  
##   [649] "V649"   "V650"   "V651"   "V652"   "V653"   "V654"   "V655"   "V656"  
##   [657] "V657"   "V658"   "V659"   "V660"   "V661"   "V662"   "V663"   "V664"  
##   [665] "V665"   "V666"   "V667"   "V668"   "V669"   "V670"   "V671"   "V672"  
##   [673] "V673"   "V674"   "V675"   "V676"   "V677"   "V678"   "V679"   "V680"  
##   [681] "V681"   "V682"   "V683"   "V684"   "V685"   "V686"   "V687"   "V688"  
##   [689] "V689"   "V690"   "V691"   "V692"   "V693"   "V694"   "V695"   "V696"  
##   [697] "V697"   "V698"   "V699"   "V700"   "V701"   "V702"   "V703"   "V704"  
##   [705] "V705"   "V706"   "V707"   "V708"   "V709"   "V710"   "V711"   "V712"  
##   [713] "V713"   "V714"   "V715"   "V716"   "V717"   "V718"   "V719"   "V720"  
##   [721] "V721"   "V722"   "V723"   "V724"   "V725"   "V726"   "V727"   "V728"  
##   [729] "V729"   "V730"   "V731"   "V732"   "V733"   "V734"   "V735"   "V736"  
##   [737] "V737"   "V738"   "V739"   "V740"   "V741"   "V742"   "V743"   "V744"  
##   [745] "V745"   "V746"   "V747"   "V748"   "V749"   "V750"   "V751"   "V752"  
##   [753] "V753"   "V754"   "V755"   "V756"   "V757"   "V758"   "V759"   "V760"  
##   [761] "V761"   "V762"   "V763"   "V764"   "V765"   "V766"   "V767"   "V768"  
##   [769] "V769"   "V770"   "V771"   "V772"   "V773"   "V774"   "V775"   "V776"  
##   [777] "V777"   "V778"   "V779"   "V780"   "V781"   "V782"   "V783"   "V784"  
##   [785] "V785"   "V786"   "V787"   "V788"   "V789"   "V790"   "V791"   "V792"  
##   [793] "V793"   "V794"   "V795"   "V796"   "V797"   "V798"   "V799"   "V800"  
##   [801] "V801"   "V802"   "V803"   "V804"   "V805"   "V806"   "V807"   "V808"  
##   [809] "V809"   "V810"   "V811"   "V812"   "V813"   "V814"   "V815"   "V816"  
##   [817] "V817"   "V818"   "V819"   "V820"   "V821"   "V822"   "V823"   "V824"  
##   [825] "V825"   "V826"   "V827"   "V828"   "V829"   "V830"   "V831"   "V832"  
##   [833] "V833"   "V834"   "V835"   "V836"   "V837"   "V838"   "V839"   "V840"  
##   [841] "V841"   "V842"   "V843"   "V844"   "V845"   "V846"   "V847"   "V848"  
##   [849] "V849"   "V850"   "V851"   "V852"   "V853"   "V854"   "V855"   "V856"  
##   [857] "V857"   "V858"   "V859"   "V860"   "V861"   "V862"   "V863"   "V864"  
##   [865] "V865"   "V866"   "V867"   "V868"   "V869"   "V870"   "V871"   "V872"  
##   [873] "V873"   "V874"   "V875"   "V876"   "V877"   "V878"   "V879"   "V880"  
##   [881] "V881"   "V882"   "V883"   "V884"   "V885"   "V886"   "V887"   "V888"  
##   [889] "V889"   "V890"   "V891"   "V892"   "V893"   "V894"   "V895"   "V896"  
##   [897] "V897"   "V898"   "V899"   "V900"   "V901"   "V902"   "V903"   "V904"  
##   [905] "V905"   "V906"   "V907"   "V908"   "V909"   "V910"   "V911"   "V912"  
##   [913] "V913"   "V914"   "V915"   "V916"   "V917"   "V918"   "V919"   "V920"  
##   [921] "V921"   "V922"   "V923"   "V924"   "V925"   "V926"   "V927"   "V928"  
##   [929] "V929"   "V930"   "V931"   "V932"   "V933"   "V934"   "V935"   "V936"  
##   [937] "V937"   "V938"   "V939"   "V940"   "V941"   "V942"   "V943"   "V944"  
##   [945] "V945"   "V946"   "V947"   "V948"   "V949"   "V950"   "V951"   "V952"  
##   [953] "V953"   "V954"   "V955"   "V956"   "V957"   "V958"   "V959"   "V960"  
##   [961] "V961"   "V962"   "V963"   "V964"   "V965"   "V966"   "V967"   "V968"  
##   [969] "V969"   "V970"   "V971"   "V972"   "V973"   "V974"   "V975"   "V976"  
##   [977] "V977"   "V978"   "V979"   "V980"   "V981"   "V982"   "V983"   "V984"  
##   [985] "V985"   "V986"   "V987"   "V988"   "V989"   "V990"   "V991"   "V992"  
##   [993] "V993"   "V994"   "V995"   "V996"   "V997"   "V998"   "V999"   "V1000" 
##  [1001] "V1001"  "V1002"  "V1003"  "V1004"  "V1005"  "V1006"  "V1007"  "V1008" 
##  [1009] "V1009"  "V1010"  "V1011"  "V1012"  "V1013"  "V1014"  "V1015"  "V1016" 
##  [1017] "V1017"  "V1018"  "V1019"  "V1020"  "V1021"  "V1022"  "V1023"  "V1024" 
##  [1025] "V1025"  "V1026"  "V1027"  "V1028"  "V1029"  "V1030"  "V1031"  "V1032" 
##  [1033] "V1033"  "V1034"  "V1035"  "V1036"  "V1037"  "V1038"  "V1039"  "V1040" 
##  [1041] "V1041"  "V1042"  "V1043"  "V1044"  "V1045"  "V1046"  "V1047"  "V1048" 
##  [1049] "V1049"  "V1050"  "V1051"  "V1052"  "V1053"  "V1054"  "V1055"  "V1056" 
##  [1057] "V1057"  "V1058"  "V1059"  "V1060"  "V1061"  "V1062"  "V1063"  "V1064" 
##  [1065] "V1065"  "V1066"  "V1067"  "V1068"  "V1069"  "V1070"  "V1071"  "V1072" 
##  [1073] "V1073"  "V1074"  "V1075"  "V1076"  "V1077"  "V1078"  "V1079"  "V1080" 
##  [1081] "V1081"  "V1082"  "V1083"  "V1084"  "V1085"  "V1086"  "V1087"  "V1088" 
##  [1089] "V1089"  "V1090"  "V1091"  "V1092"  "V1093"  "V1094"  "V1095"  "V1096" 
##  [1097] "V1097"  "V1098"  "V1099"  "V1100"  "V1101"  "V1102"  "V1103"  "V1104" 
##  [1105] "V1105"  "V1106"  "V1107"  "V1108"  "V1109"  "V1110"  "V1111"  "V1112" 
##  [1113] "V1113"  "V1114"  "V1115"  "V1116"  "V1117"  "V1118"  "V1119"  "V1120" 
##  [1121] "V1121"  "V1122"  "V1123"  "V1124"  "V1125"  "V1126"  "V1127"  "V1128" 
##  [1129] "V1129"  "V1130"  "V1131"  "V1132"  "V1133"  "V1134"  "V1135"  "V1136" 
##  [1137] "V1137"  "V1138"  "V1139"  "V1140"  "V1141"  "V1142"  "V1143"  "V1144" 
##  [1145] "V1145"  "V1146"  "V1147"  "V1148"  "V1149"  "V1150"  "V1151"  "V1152" 
##  [1153] "V1153"  "V1154"  "V1155"  "V1156"  "V1157"  "V1158"  "V1159"  "V1160" 
##  [1161] "V1161"  "V1162"  "V1163"  "V1164"  "V1165"  "V1166"  "V1167"  "V1168" 
##  [1169] "V1169"  "V1170"  "V1171"  "V1172"  "V1173"  "V1174"  "V1175"  "V1176" 
##  [1177] "V1177"  "V1178"  "V1179"  "V1180"  "V1181"  "V1182"  "V1183"  "V1184" 
##  [1185] "V1185"  "V1186"  "V1187"  "V1188"  "V1189"  "V1190"  "V1191"  "V1192" 
##  [1193] "V1193"  "V1194"  "V1195"  "V1196"  "V1197"  "V1198"  "V1199"  "V1200" 
##  [1201] "V1201"  "V1202"  "V1203"  "V1204"  "V1205"  "V1206"  "V1207"  "V1208" 
##  [1209] "V1209"  "V1210"  "V1211"  "V1212"  "V1213"  "V1214"  "V1215"  "V1216" 
##  [1217] "V1217"  "V1218"  "V1219"  "V1220"  "V1221"  "V1222"  "V1223"  "V1224" 
##  [1225] "V1225"  "V1226"  "V1227"  "V1228"  "V1229"  "V1230"  "V1231"  "V1232" 
##  [1233] "V1233"  "V1234"  "V1235"  "V1236"  "V1237"  "V1238"  "V1239"  "V1240" 
##  [1241] "V1241"  "V1242"  "V1243"  "V1244"  "V1245"  "V1246"  "V1247"  "V1248" 
##  [1249] "V1249"  "V1250"  "V1251"  "V1252"  "V1253"  "V1254"  "V1255"  "V1256" 
##  [1257] "V1257"  "V1258"  "V1259"  "V1260"  "V1261"  "V1262"  "V1263"  "V1264" 
##  [1265] "V1265"  "V1266"  "V1267"  "V1268"  "V1269"  "V1270"  "V1271"  "V1272" 
##  [1273] "V1273"  "V1274"  "V1275"  "V1276"  "V1277"  "V1278"  "V1279"  "V1280" 
##  [1281] "V1281"  "V1282"  "V1283"  "V1284"  "V1285"  "V1286"  "V1287"  "V1288" 
##  [1289] "V1289"  "V1290"  "V1291"  "V1292"  "V1293"  "V1294"  "V1295"  "V1296" 
##  [1297] "V1297"  "V1298"  "V1299"  "V1300"  "V1301"  "V1302"  "V1303"  "V1304" 
##  [1305] "V1305"  "V1306"  "V1307"  "V1308"  "V1309"  "V1310"  "V1311"  "V1312" 
##  [1313] "V1313"  "V1314"  "V1315"  "V1316"  "V1317"  "V1318"  "V1319"  "V1320" 
##  [1321] "V1321"  "V1322"  "V1323"  "V1324"  "V1325"  "V1326"  "V1327"  "V1328" 
##  [1329] "V1329"  "V1330"  "V1331"  "V1332"  "V1333"  "V1334"  "V1335"  "V1336" 
##  [1337] "V1337"  "V1338"  "V1339"  "V1340"  "V1341"  "V1342"  "V1343"  "V1344" 
##  [1345] "V1345"  "V1346"  "V1347"  "V1348"  "V1349"  "V1350"  "V1351"  "V1352" 
##  [1353] "V1353"  "V1354"  "V1355"  "V1356"  "V1357"  "V1358"  "V1359"  "V1360" 
##  [1361] "V1361"  "V1362"  "V1363"  "V1364"  "V1365"  "V1366"  "V1367"  "V1368" 
##  [1369] "V1369"  "V1370"  "V1371"  "V1372"  "V1373"  "V1374"  "V1375"  "V1376" 
##  [1377] "V1377"  "V1378"  "V1379"  "V1380"  "V1381"  "V1382"  "V1383"  "V1384" 
##  [1385] "V1385"  "V1386"  "V1387"  "V1388"  "V1389"  "V1390"  "V1391"  "V1392" 
##  [1393] "V1393"  "V1394"  "V1395"  "V1396"  "V1397"  "V1398"  "V1399"  "V1400" 
##  [1401] "V1401"  "V1402"  "V1403"  "V1404"  "V1405"  "V1406"  "V1407"  "V1408" 
##  [1409] "V1409"  "V1410"  "V1411"  "V1412"  "V1413"  "V1414"  "V1415"  "V1416" 
##  [1417] "V1417"  "V1418"  "V1419"  "V1420"  "V1421"  "V1422"  "V1423"  "V1424" 
##  [1425] "V1425"  "V1426"  "V1427"  "V1428"  "V1429"  "V1430"  "V1431"  "V1432" 
##  [1433] "V1433"  "V1434"  "V1435"  "V1436"  "V1437"  "V1438"  "V1439"  "V1440" 
##  [1441] "V1441"  "V1442"  "V1443"  "V1444"  "V1445"  "V1446"  "V1447"  "V1448" 
##  [1449] "V1449"  "V1450"  "V1451"  "V1452"  "V1453"  "V1454"  "V1455"  "V1456" 
##  [1457] "V1457"  "V1458"  "V1459"  "V1460"  "V1461"  "V1462"  "V1463"  "V1464" 
##  [1465] "V1465"  "V1466"  "V1467"  "V1468"  "V1469"  "V1470"  "V1471"  "V1472" 
##  [1473] "V1473"  "V1474"  "V1475"  "V1476"  "V1477"  "V1478"  "V1479"  "V1480" 
##  [1481] "V1481"  "V1482"  "V1483"  "V1484"  "V1485"  "V1486"  "V1487"  "V1488" 
##  [1489] "V1489"  "V1490"  "V1491"  "V1492"  "V1493"  "V1494"  "V1495"  "V1496" 
##  [1497] "V1497"  "V1498"  "V1499"  "V1500"  "V1501"  "V1502"  "V1503"  "V1504" 
##  [1505] "V1505"  "V1506"  "V1507"  "V1508"  "V1509"  "V1510"  "V1511"  "V1512" 
##  [1513] "V1513"  "V1514"  "V1515"  "V1516"  "V1517"  "V1518"  "V1519"  "V1520" 
##  [1521] "V1521"  "V1522"  "V1523"  "V1524"  "V1525"  "V1526"  "V1527"  "V1528" 
##  [1529] "V1529"  "V1530"  "V1531"  "V1532"  "V1533"  "V1534"  "V1535"  "V1536" 
##  [1537] "V1537"  "V1538"  "V1539"  "V1540"  "V1541"  "V1542"  "V1543"  "V1544" 
##  [1545] "V1545"  "V1546"  "V1547"  "V1548"  "V1549"  "V1550"  "V1551"  "V1552" 
##  [1553] "V1553"  "V1554"  "V1555"  "V1556"  "V1557"  "V1558"  "V1559"  "V1560" 
##  [1561] "V1561"  "V1562"  "V1563"  "V1564"  "V1565"  "V1566"  "V1567"  "V1568" 
##  [1569] "V1569"  "V1570"  "V1571"  "V1572"  "V1573"  "V1574"  "V1575"  "V1576" 
##  [1577] "V1577"  "V1578"  "V1579"  "V1580"  "V1581"  "V1582"  "V1583"  "V1584" 
##  [1585] "V1585"  "V1586"  "V1587"  "V1588"  "V1589"  "V1590"  "V1591"  "V1592" 
##  [1593] "V1593"  "V1594"  "V1595"  "V1596"  "V1597"  "V1598"  "V1599"  "V1600" 
##  [1601] "V1601"  "V1602"  "V1603"  "V1604"  "V1605"  "V1606"  "V1607"  "V1608" 
##  [1609] "V1609"  "V1610"  "V1611"  "V1612"  "V1613"  "V1614"  "V1615"  "V1616" 
##  [1617] "V1617"  "V1618"  "V1619"  "V1620"  "V1621"  "V1622"  "V1623"  "V1624" 
##  [1625] "V1625"  "V1626"  "V1627"  "V1628"  "V1629"  "V1630"  "V1631"  "V1632" 
##  [1633] "V1633"  "V1634"  "V1635"  "V1636"  "V1637"  "V1638"  "V1639"  "V1640" 
##  [1641] "V1641"  "V1642"  "V1643"  "V1644"  "V1645"  "V1646"  "V1647"  "V1648" 
##  [1649] "V1649"  "V1650"  "V1651"  "V1652"  "V1653"  "V1654"  "V1655"  "V1656" 
##  [1657] "V1657"  "V1658"  "V1659"  "V1660"  "V1661"  "V1662"  "V1663"  "V1664" 
##  [1665] "V1665"  "V1666"  "V1667"  "V1668"  "V1669"  "V1670"  "V1671"  "V1672" 
##  [1673] "V1673"  "V1674"  "V1675"  "V1676"  "V1677"  "V1678"  "V1679"  "V1680" 
##  [1681] "V1681"  "V1682"  "V1683"  "V1684"  "V1685"  "V1686"  "V1687"  "V1688" 
##  [1689] "V1689"  "V1690"  "V1691"  "V1692"  "V1693"  "V1694"  "V1695"  "V1696" 
##  [1697] "V1697"  "V1698"  "V1699"  "V1700"  "V1701"  "V1702"  "V1703"  "V1704" 
##  [1705] "V1705"  "V1706"  "V1707"  "V1708"  "V1709"  "V1710"  "V1711"  "V1712" 
##  [1713] "V1713"  "V1714"  "V1715"  "V1716"  "V1717"  "V1718"  "V1719"  "V1720" 
##  [1721] "V1721"  "V1722"  "V1723"  "V1724"  "V1725"  "V1726"  "V1727"  "V1728" 
##  [1729] "V1729"  "V1730"  "V1731"  "V1732"  "V1733"  "V1734"  "V1735"  "V1736" 
##  [1737] "V1737"  "V1738"  "V1739"  "V1740"  "V1741"  "V1742"  "V1743"  "V1744" 
##  [1745] "V1745"  "V1746"  "V1747"  "V1748"  "V1749"  "V1750"  "V1751"  "V1752" 
##  [1753] "V1753"  "V1754"  "V1755"  "V1756"  "V1757"  "V1758"  "V1759"  "V1760" 
##  [1761] "V1761"  "V1762"  "V1763"  "V1764"  "V1765"  "V1766"  "V1767"  "V1768" 
##  [1769] "V1769"  "V1770"  "V1771"  "V1772"  "V1773"  "V1774"  "V1775"  "V1776" 
##  [1777] "V1777"  "V1778"  "V1779"  "V1780"  "V1781"  "V1782"  "V1783"  "V1784" 
##  [1785] "V1785"  "V1786"  "V1787"  "V1788"  "V1789"  "V1790"  "V1791"  "V1792" 
##  [1793] "V1793"  "V1794"  "V1795"  "V1796"  "V1797"  "V1798"  "V1799"  "V1800" 
##  [1801] "V1801"  "V1802"  "V1803"  "V1804"  "V1805"  "V1806"  "V1807"  "V1808" 
##  [1809] "V1809"  "V1810"  "V1811"  "V1812"  "V1813"  "V1814"  "V1815"  "V1816" 
##  [1817] "V1817"  "V1818"  "V1819"  "V1820"  "V1821"  "V1822"  "V1823"  "V1824" 
##  [1825] "V1825"  "V1826"  "V1827"  "V1828"  "V1829"  "V1830"  "V1831"  "V1832" 
##  [1833] "V1833"  "V1834"  "V1835"  "V1836"  "V1837"  "V1838"  "V1839"  "V1840" 
##  [1841] "V1841"  "V1842"  "V1843"  "V1844"  "V1845"  "V1846"  "V1847"  "V1848" 
##  [1849] "V1849"  "V1850"  "V1851"  "V1852"  "V1853"  "V1854"  "V1855"  "V1856" 
##  [1857] "V1857"  "V1858"  "V1859"  "V1860"  "V1861"  "V1862"  "V1863"  "V1864" 
##  [1865] "V1865"  "V1866"  "V1867"  "V1868"  "V1869"  "V1870"  "V1871"  "V1872" 
##  [1873] "V1873"  "V1874"  "V1875"  "V1876"  "V1877"  "V1878"  "V1879"  "V1880" 
##  [1881] "V1881"  "V1882"  "V1883"  "V1884"  "V1885"  "V1886"  "V1887"  "V1888" 
##  [1889] "V1889"  "V1890"  "V1891"  "V1892"  "V1893"  "V1894"  "V1895"  "V1896" 
##  [1897] "V1897"  "V1898"  "V1899"  "V1900"  "V1901"  "V1902"  "V1903"  "V1904" 
##  [1905] "V1905"  "V1906"  "V1907"  "V1908"  "V1909"  "V1910"  "V1911"  "V1912" 
##  [1913] "V1913"  "V1914"  "V1915"  "V1916"  "V1917"  "V1918"  "V1919"  "V1920" 
##  [1921] "V1921"  "V1922"  "V1923"  "V1924"  "V1925"  "V1926"  "V1927"  "V1928" 
##  [1929] "V1929"  "V1930"  "V1931"  "V1932"  "V1933"  "V1934"  "V1935"  "V1936" 
##  [1937] "V1937"  "V1938"  "V1939"  "V1940"  "V1941"  "V1942"  "V1943"  "V1944" 
##  [1945] "V1945"  "V1946"  "V1947"  "V1948"  "V1949"  "V1950"  "V1951"  "V1952" 
##  [1953] "V1953"  "V1954"  "V1955"  "V1956"  "V1957"  "V1958"  "V1959"  "V1960" 
##  [1961] "V1961"  "V1962"  "V1963"  "V1964"  "V1965"  "V1966"  "V1967"  "V1968" 
##  [1969] "V1969"  "V1970"  "V1971"  "V1972"  "V1973"  "V1974"  "V1975"  "V1976" 
##  [1977] "V1977"  "V1978"  "V1979"  "V1980"  "V1981"  "V1982"  "V1983"  "V1984" 
##  [1985] "V1985"  "V1986"  "V1987"  "V1988"  "V1989"  "V1990"  "V1991"  "V1992" 
##  [1993] "V1993"  "V1994"  "V1995"  "V1996"  "V1997"  "V1998"  "V1999"  "V2000" 
##  [2001] "V2001"  "V2002"  "V2003"  "V2004"  "V2005"  "V2006"  "V2007"  "V2008" 
##  [2009] "V2009"  "V2010"  "V2011"  "V2012"  "V2013"  "V2014"  "V2015"  "V2016" 
##  [2017] "V2017"  "V2018"  "V2019"  "V2020"  "V2021"  "V2022"  "V2023"  "V2024" 
##  [2025] "V2025"  "V2026"  "V2027"  "V2028"  "V2029"  "V2030"  "V2031"  "V2032" 
##  [2033] "V2033"  "V2034"  "V2035"  "V2036"  "V2037"  "V2038"  "V2039"  "V2040" 
##  [2041] "V2041"  "V2042"  "V2043"  "V2044"  "V2045"  "V2046"  "V2047"  "V2048" 
##  [2049] "V2049"  "V2050"  "V2051"  "V2052"  "V2053"  "V2054"  "V2055"  "V2056" 
##  [2057] "V2057"  "V2058"  "V2059"  "V2060"  "V2061"  "V2062"  "V2063"  "V2064" 
##  [2065] "V2065"  "V2066"  "V2067"  "V2068"  "V2069"  "V2070"  "V2071"  "V2072" 
##  [2073] "V2073"  "V2074"  "V2075"  "V2076"  "V2077"  "V2078"  "V2079"  "V2080" 
##  [2081] "V2081"  "V2082"  "V2083"  "V2084"  "V2085"  "V2086"  "V2087"  "V2088" 
##  [2089] "V2089"  "V2090"  "V2091"  "V2092"  "V2093"  "V2094"  "V2095"  "V2096" 
##  [2097] "V2097"  "V2098"  "V2099"  "V2100"  "V2101"  "V2102"  "V2103"  "V2104" 
##  [2105] "V2105"  "V2106"  "V2107"  "V2108"  "V2109"  "V2110"  "V2111"  "V2112" 
##  [2113] "V2113"  "V2114"  "V2115"  "V2116"  "V2117"  "V2118"  "V2119"  "V2120" 
##  [2121] "V2121"  "V2122"  "V2123"  "V2124"  "V2125"  "V2126"  "V2127"  "V2128" 
##  [2129] "V2129"  "V2130"  "V2131"  "V2132"  "V2133"  "V2134"  "V2135"  "V2136" 
##  [2137] "V2137"  "V2138"  "V2139"  "V2140"  "V2141"  "V2142"  "V2143"  "V2144" 
##  [2145] "V2145"  "V2146"  "V2147"  "V2148"  "V2149"  "V2150"  "V2151"  "V2152" 
##  [2153] "V2153"  "V2154"  "V2155"  "V2156"  "V2157"  "V2158"  "V2159"  "V2160" 
##  [2161] "V2161"  "V2162"  "V2163"  "V2164"  "V2165"  "V2166"  "V2167"  "V2168" 
##  [2169] "V2169"  "V2170"  "V2171"  "V2172"  "V2173"  "V2174"  "V2175"  "V2176" 
##  [2177] "V2177"  "V2178"  "V2179"  "V2180"  "V2181"  "V2182"  "V2183"  "V2184" 
##  [2185] "V2185"  "V2186"  "V2187"  "V2188"  "V2189"  "V2190"  "V2191"  "V2192" 
##  [2193] "V2193"  "V2194"  "V2195"  "V2196"  "V2197"  "V2198"  "V2199"  "V2200" 
##  [2201] "V2201"  "V2202"  "V2203"  "V2204"  "V2205"  "V2206"  "V2207"  "V2208" 
##  [2209] "V2209"  "V2210"  "V2211"  "V2212"  "V2213"  "V2214"  "V2215"  "V2216" 
##  [2217] "V2217"  "V2218"  "V2219"  "V2220"  "V2221"  "V2222"  "V2223"  "V2224" 
##  [2225] "V2225"  "V2226"  "V2227"  "V2228"  "V2229"  "V2230"  "V2231"  "V2232" 
##  [2233] "V2233"  "V2234"  "V2235"  "V2236"  "V2237"  "V2238"  "V2239"  "V2240" 
##  [2241] "V2241"  "V2242"  "V2243"  "V2244"  "V2245"  "V2246"  "V2247"  "V2248" 
##  [2249] "V2249"  "V2250"  "V2251"  "V2252"  "V2253"  "V2254"  "V2255"  "V2256" 
##  [2257] "V2257"  "V2258"  "V2259"  "V2260"  "V2261"  "V2262"  "V2263"  "V2264" 
##  [2265] "V2265"  "V2266"  "V2267"  "V2268"  "V2269"  "V2270"  "V2271"  "V2272" 
##  [2273] "V2273"  "V2274"  "V2275"  "V2276"  "V2277"  "V2278"  "V2279"  "V2280" 
##  [2281] "V2281"  "V2282"  "V2283"  "V2284"  "V2285"  "V2286"  "V2287"  "V2288" 
##  [2289] "V2289"  "V2290"  "V2291"  "V2292"  "V2293"  "V2294"  "V2295"  "V2296" 
##  [2297] "V2297"  "V2298"  "V2299"  "V2300"  "V2301"  "V2302"  "V2303"  "V2304" 
##  [2305] "V2305"  "V2306"  "V2307"  "V2308"  "V2309"  "V2310"  "V2311"  "V2312" 
##  [2313] "V2313"  "V2314"  "V2315"  "V2316"  "V2317"  "V2318"  "V2319"  "V2320" 
##  [2321] "V2321"  "V2322"  "V2323"  "V2324"  "V2325"  "V2326"  "V2327"  "V2328" 
##  [2329] "V2329"  "V2330"  "V2331"  "V2332"  "V2333"  "V2334"  "V2335"  "V2336" 
##  [2337] "V2337"  "V2338"  "V2339"  "V2340"  "V2341"  "V2342"  "V2343"  "V2344" 
##  [2345] "V2345"  "V2346"  "V2347"  "V2348"  "V2349"  "V2350"  "V2351"  "V2352" 
##  [2353] "V2353"  "V2354"  "V2355"  "V2356"  "V2357"  "V2358"  "V2359"  "V2360" 
##  [2361] "V2361"  "V2362"  "V2363"  "V2364"  "V2365"  "V2366"  "V2367"  "V2368" 
##  [2369] "V2369"  "V2370"  "V2371"  "V2372"  "V2373"  "V2374"  "V2375"  "V2376" 
##  [2377] "V2377"  "V2378"  "V2379"  "V2380"  "V2381"  "V2382"  "V2383"  "V2384" 
##  [2385] "V2385"  "V2386"  "V2387"  "V2388"  "V2389"  "V2390"  "V2391"  "V2392" 
##  [2393] "V2393"  "V2394"  "V2395"  "V2396"  "V2397"  "V2398"  "V2399"  "V2400" 
##  [2401] "V2401"  "V2402"  "V2403"  "V2404"  "V2405"  "V2406"  "V2407"  "V2408" 
##  [2409] "V2409"  "V2410"  "V2411"  "V2412"  "V2413"  "V2414"  "V2415"  "V2416" 
##  [2417] "V2417"  "V2418"  "V2419"  "V2420"  "V2421"  "V2422"  "V2423"  "V2424" 
##  [2425] "V2425"  "V2426"  "V2427"  "V2428"  "V2429"  "V2430"  "V2431"  "V2432" 
##  [2433] "V2433"  "V2434"  "V2435"  "V2436"  "V2437"  "V2438"  "V2439"  "V2440" 
##  [2441] "V2441"  "V2442"  "V2443"  "V2444"  "V2445"  "V2446"  "V2447"  "V2448" 
##  [2449] "V2449"  "V2450"  "V2451"  "V2452"  "V2453"  "V2454"  "V2455"  "V2456" 
##  [2457] "V2457"  "V2458"  "V2459"  "V2460"  "V2461"  "V2462"  "V2463"  "V2464" 
##  [2465] "V2465"  "V2466"  "V2467"  "V2468"  "V2469"  "V2470"  "V2471"  "V2472" 
##  [2473] "V2473"  "V2474"  "V2475"  "V2476"  "V2477"  "V2478"  "V2479"  "V2480" 
##  [2481] "V2481"  "V2482"  "V2483"  "V2484"  "V2485"  "V2486"  "V2487"  "V2488" 
##  [2489] "V2489"  "V2490"  "V2491"  "V2492"  "V2493"  "V2494"  "V2495"  "V2496" 
##  [2497] "V2497"  "V2498"  "V2499"  "V2500"  "V2501"  "V2502"  "V2503"  "V2504" 
##  [2505] "V2505"  "V2506"  "V2507"  "V2508"  "V2509"  "V2510"  "V2511"  "V2512" 
##  [2513] "V2513"  "V2514"  "V2515"  "V2516"  "V2517"  "V2518"  "V2519"  "V2520" 
##  [2521] "V2521"  "V2522"  "V2523"  "V2524"  "V2525"  "V2526"  "V2527"  "V2528" 
##  [2529] "V2529"  "V2530"  "V2531"  "V2532"  "V2533"  "V2534"  "V2535"  "V2536" 
##  [2537] "V2537"  "V2538"  "V2539"  "V2540"  "V2541"  "V2542"  "V2543"  "V2544" 
##  [2545] "V2545"  "V2546"  "V2547"  "V2548"  "V2549"  "V2550"  "V2551"  "V2552" 
##  [2553] "V2553"  "V2554"  "V2555"  "V2556"  "V2557"  "V2558"  "V2559"  "V2560" 
##  [2561] "V2561"  "V2562"  "V2563"  "V2564"  "V2565"  "V2566"  "V2567"  "V2568" 
##  [2569] "V2569"  "V2570"  "V2571"  "V2572"  "V2573"  "V2574"  "V2575"  "V2576" 
##  [2577] "V2577"  "V2578"  "V2579"  "V2580"  "V2581"  "V2582"  "V2583"  "V2584" 
##  [2585] "V2585"  "V2586"  "V2587"  "V2588"  "V2589"  "V2590"  "V2591"  "V2592" 
##  [2593] "V2593"  "V2594"  "V2595"  "V2596"  "V2597"  "V2598"  "V2599"  "V2600" 
##  [2601] "V2601"  "V2602"  "V2603"  "V2604"  "V2605"  "V2606"  "V2607"  "V2608" 
##  [2609] "V2609"  "V2610"  "V2611"  "V2612"  "V2613"  "V2614"  "V2615"  "V2616" 
##  [2617] "V2617"  "V2618"  "V2619"  "V2620"  "V2621"  "V2622"  "V2623"  "V2624" 
##  [2625] "V2625"  "V2626"  "V2627"  "V2628"  "V2629"  "V2630"  "V2631"  "V2632" 
##  [2633] "V2633"  "V2634"  "V2635"  "V2636"  "V2637"  "V2638"  "V2639"  "V2640" 
##  [2641] "V2641"  "V2642"  "V2643"  "V2644"  "V2645"  "V2646"  "V2647"  "V2648" 
##  [2649] "V2649"  "V2650"  "V2651"  "V2652"  "V2653"  "V2654"  "V2655"  "V2656" 
##  [2657] "V2657"  "V2658"  "V2659"  "V2660"  "V2661"  "V2662"  "V2663"  "V2664" 
##  [2665] "V2665"  "V2666"  "V2667"  "V2668"  "V2669"  "V2670"  "V2671"  "V2672" 
##  [2673] "V2673"  "V2674"  "V2675"  "V2676"  "V2677"  "V2678"  "V2679"  "V2680" 
##  [2681] "V2681"  "V2682"  "V2683"  "V2684"  "V2685"  "V2686"  "V2687"  "V2688" 
##  [2689] "V2689"  "V2690"  "V2691"  "V2692"  "V2693"  "V2694"  "V2695"  "V2696" 
##  [2697] "V2697"  "V2698"  "V2699"  "V2700"  "V2701"  "V2702"  "V2703"  "V2704" 
##  [2705] "V2705"  "V2706"  "V2707"  "V2708"  "V2709"  "V2710"  "V2711"  "V2712" 
##  [2713] "V2713"  "V2714"  "V2715"  "V2716"  "V2717"  "V2718"  "V2719"  "V2720" 
##  [2721] "V2721"  "V2722"  "V2723"  "V2724"  "V2725"  "V2726"  "V2727"  "V2728" 
##  [2729] "V2729"  "V2730"  "V2731"  "V2732"  "V2733"  "V2734"  "V2735"  "V2736" 
##  [2737] "V2737"  "V2738"  "V2739"  "V2740"  "V2741"  "V2742"  "V2743"  "V2744" 
##  [2745] "V2745"  "V2746"  "V2747"  "V2748"  "V2749"  "V2750"  "V2751"  "V2752" 
##  [2753] "V2753"  "V2754"  "V2755"  "V2756"  "V2757"  "V2758"  "V2759"  "V2760" 
##  [2761] "V2761"  "V2762"  "V2763"  "V2764"  "V2765"  "V2766"  "V2767"  "V2768" 
##  [2769] "V2769"  "V2770"  "V2771"  "V2772"  "V2773"  "V2774"  "V2775"  "V2776" 
##  [2777] "V2777"  "V2778"  "V2779"  "V2780"  "V2781"  "V2782"  "V2783"  "V2784" 
##  [2785] "V2785"  "V2786"  "V2787"  "V2788"  "V2789"  "V2790"  "V2791"  "V2792" 
##  [2793] "V2793"  "V2794"  "V2795"  "V2796"  "V2797"  "V2798"  "V2799"  "V2800" 
##  [2801] "V2801"  "V2802"  "V2803"  "V2804"  "V2805"  "V2806"  "V2807"  "V2808" 
##  [2809] "V2809"  "V2810"  "V2811"  "V2812"  "V2813"  "V2814"  "V2815"  "V2816" 
##  [2817] "V2817"  "V2818"  "V2819"  "V2820"  "V2821"  "V2822"  "V2823"  "V2824" 
##  [2825] "V2825"  "V2826"  "V2827"  "V2828"  "V2829"  "V2830"  "V2831"  "V2832" 
##  [2833] "V2833"  "V2834"  "V2835"  "V2836"  "V2837"  "V2838"  "V2839"  "V2840" 
##  [2841] "V2841"  "V2842"  "V2843"  "V2844"  "V2845"  "V2846"  "V2847"  "V2848" 
##  [2849] "V2849"  "V2850"  "V2851"  "V2852"  "V2853"  "V2854"  "V2855"  "V2856" 
##  [2857] "V2857"  "V2858"  "V2859"  "V2860"  "V2861"  "V2862"  "V2863"  "V2864" 
##  [2865] "V2865"  "V2866"  "V2867"  "V2868"  "V2869"  "V2870"  "V2871"  "V2872" 
##  [2873] "V2873"  "V2874"  "V2875"  "V2876"  "V2877"  "V2878"  "V2879"  "V2880" 
##  [2881] "V2881"  "V2882"  "V2883"  "V2884"  "V2885"  "V2886"  "V2887"  "V2888" 
##  [2889] "V2889"  "V2890"  "V2891"  "V2892"  "V2893"  "V2894"  "V2895"  "V2896" 
##  [2897] "V2897"  "V2898"  "V2899"  "V2900"  "V2901"  "V2902"  "V2903"  "V2904" 
##  [2905] "V2905"  "V2906"  "V2907"  "V2908"  "V2909"  "V2910"  "V2911"  "V2912" 
##  [2913] "V2913"  "V2914"  "V2915"  "V2916"  "V2917"  "V2918"  "V2919"  "V2920" 
##  [2921] "V2921"  "V2922"  "V2923"  "V2924"  "V2925"  "V2926"  "V2927"  "V2928" 
##  [2929] "V2929"  "V2930"  "V2931"  "V2932"  "V2933"  "V2934"  "V2935"  "V2936" 
##  [2937] "V2937"  "V2938"  "V2939"  "V2940"  "V2941"  "V2942"  "V2943"  "V2944" 
##  [2945] "V2945"  "V2946"  "V2947"  "V2948"  "V2949"  "V2950"  "V2951"  "V2952" 
##  [2953] "V2953"  "V2954"  "V2955"  "V2956"  "V2957"  "V2958"  "V2959"  "V2960" 
##  [2961] "V2961"  "V2962"  "V2963"  "V2964"  "V2965"  "V2966"  "V2967"  "V2968" 
##  [2969] "V2969"  "V2970"  "V2971"  "V2972"  "V2973"  "V2974"  "V2975"  "V2976" 
##  [2977] "V2977"  "V2978"  "V2979"  "V2980"  "V2981"  "V2982"  "V2983"  "V2984" 
##  [2985] "V2985"  "V2986"  "V2987"  "V2988"  "V2989"  "V2990"  "V2991"  "V2992" 
##  [2993] "V2993"  "V2994"  "V2995"  "V2996"  "V2997"  "V2998"  "V2999"  "V3000" 
##  [3001] "V3001"  "V3002"  "V3003"  "V3004"  "V3005"  "V3006"  "V3007"  "V3008" 
##  [3009] "V3009"  "V3010"  "V3011"  "V3012"  "V3013"  "V3014"  "V3015"  "V3016" 
##  [3017] "V3017"  "V3018"  "V3019"  "V3020"  "V3021"  "V3022"  "V3023"  "V3024" 
##  [3025] "V3025"  "V3026"  "V3027"  "V3028"  "V3029"  "V3030"  "V3031"  "V3032" 
##  [3033] "V3033"  "V3034"  "V3035"  "V3036"  "V3037"  "V3038"  "V3039"  "V3040" 
##  [3041] "V3041"  "V3042"  "V3043"  "V3044"  "V3045"  "V3046"  "V3047"  "V3048" 
##  [3049] "V3049"  "V3050"  "V3051"  "V3052"  "V3053"  "V3054"  "V3055"  "V3056" 
##  [3057] "V3057"  "V3058"  "V3059"  "V3060"  "V3061"  "V3062"  "V3063"  "V3064" 
##  [3065] "V3065"  "V3066"  "V3067"  "V3068"  "V3069"  "V3070"  "V3071"  "V3072" 
##  [3073] "V3073"  "V3074"  "V3075"  "V3076"  "V3077"  "V3078"  "V3079"  "V3080" 
##  [3081] "V3081"  "V3082"  "V3083"  "V3084"  "V3085"  "V3086"  "V3087"  "V3088" 
##  [3089] "V3089"  "V3090"  "V3091"  "V3092"  "V3093"  "V3094"  "V3095"  "V3096" 
##  [3097] "V3097"  "V3098"  "V3099"  "V3100"  "V3101"  "V3102"  "V3103"  "V3104" 
##  [3105] "V3105"  "V3106"  "V3107"  "V3108"  "V3109"  "V3110"  "V3111"  "V3112" 
##  [3113] "V3113"  "V3114"  "V3115"  "V3116"  "V3117"  "V3118"  "V3119"  "V3120" 
##  [3121] "V3121"  "V3122"  "V3123"  "V3124"  "V3125"  "V3126"  "V3127"  "V3128" 
##  [3129] "V3129"  "V3130"  "V3131"  "V3132"  "V3133"  "V3134"  "V3135"  "V3136" 
##  [3137] "V3137"  "V3138"  "V3139"  "V3140"  "V3141"  "V3142"  "V3143"  "V3144" 
##  [3145] "V3145"  "V3146"  "V3147"  "V3148"  "V3149"  "V3150"  "V3151"  "V3152" 
##  [3153] "V3153"  "V3154"  "V3155"  "V3156"  "V3157"  "V3158"  "V3159"  "V3160" 
##  [3161] "V3161"  "V3162"  "V3163"  "V3164"  "V3165"  "V3166"  "V3167"  "V3168" 
##  [3169] "V3169"  "V3170"  "V3171"  "V3172"  "V3173"  "V3174"  "V3175"  "V3176" 
##  [3177] "V3177"  "V3178"  "V3179"  "V3180"  "V3181"  "V3182"  "V3183"  "V3184" 
##  [3185] "V3185"  "V3186"  "V3187"  "V3188"  "V3189"  "V3190"  "V3191"  "V3192" 
##  [3193] "V3193"  "V3194"  "V3195"  "V3196"  "V3197"  "V3198"  "V3199"  "V3200" 
##  [3201] "V3201"  "V3202"  "V3203"  "V3204"  "V3205"  "V3206"  "V3207"  "V3208" 
##  [3209] "V3209"  "V3210"  "V3211"  "V3212"  "V3213"  "V3214"  "V3215"  "V3216" 
##  [3217] "V3217"  "V3218"  "V3219"  "V3220"  "V3221"  "V3222"  "V3223"  "V3224" 
##  [3225] "V3225"  "V3226"  "V3227"  "V3228"  "V3229"  "V3230"  "V3231"  "V3232" 
##  [3233] "V3233"  "V3234"  "V3235"  "V3236"  "V3237"  "V3238"  "V3239"  "V3240" 
##  [3241] "V3241"  "V3242"  "V3243"  "V3244"  "V3245"  "V3246"  "V3247"  "V3248" 
##  [3249] "V3249"  "V3250"  "V3251"  "V3252"  "V3253"  "V3254"  "V3255"  "V3256" 
##  [3257] "V3257"  "V3258"  "V3259"  "V3260"  "V3261"  "V3262"  "V3263"  "V3264" 
##  [3265] "V3265"  "V3266"  "V3267"  "V3268"  "V3269"  "V3270"  "V3271"  "V3272" 
##  [3273] "V3273"  "V3274"  "V3275"  "V3276"  "V3277"  "V3278"  "V3279"  "V3280" 
##  [3281] "V3281"  "V3282"  "V3283"  "V3284"  "V3285"  "V3286"  "V3287"  "V3288" 
##  [3289] "V3289"  "V3290"  "V3291"  "V3292"  "V3293"  "V3294"  "V3295"  "V3296" 
##  [3297] "V3297"  "V3298"  "V3299"  "V3300"  "V3301"  "V3302"  "V3303"  "V3304" 
##  [3305] "V3305"  "V3306"  "V3307"  "V3308"  "V3309"  "V3310"  "V3311"  "V3312" 
##  [3313] "V3313"  "V3314"  "V3315"  "V3316"  "V3317"  "V3318"  "V3319"  "V3320" 
##  [3321] "V3321"  "V3322"  "V3323"  "V3324"  "V3325"  "V3326"  "V3327"  "V3328" 
##  [3329] "V3329"  "V3330"  "V3331"  "V3332"  "V3333"  "V3334"  "V3335"  "V3336" 
##  [3337] "V3337"  "V3338"  "V3339"  "V3340"  "V3341"  "V3342"  "V3343"  "V3344" 
##  [3345] "V3345"  "V3346"  "V3347"  "V3348"  "V3349"  "V3350"  "V3351"  "V3352" 
##  [3353] "V3353"  "V3354"  "V3355"  "V3356"  "V3357"  "V3358"  "V3359"  "V3360" 
##  [3361] "V3361"  "V3362"  "V3363"  "V3364"  "V3365"  "V3366"  "V3367"  "V3368" 
##  [3369] "V3369"  "V3370"  "V3371"  "V3372"  "V3373"  "V3374"  "V3375"  "V3376" 
##  [3377] "V3377"  "V3378"  "V3379"  "V3380"  "V3381"  "V3382"  "V3383"  "V3384" 
##  [3385] "V3385"  "V3386"  "V3387"  "V3388"  "V3389"  "V3390"  "V3391"  "V3392" 
##  [3393] "V3393"  "V3394"  "V3395"  "V3396"  "V3397"  "V3398"  "V3399"  "V3400" 
##  [3401] "V3401"  "V3402"  "V3403"  "V3404"  "V3405"  "V3406"  "V3407"  "V3408" 
##  [3409] "V3409"  "V3410"  "V3411"  "V3412"  "V3413"  "V3414"  "V3415"  "V3416" 
##  [3417] "V3417"  "V3418"  "V3419"  "V3420"  "V3421"  "V3422"  "V3423"  "V3424" 
##  [3425] "V3425"  "V3426"  "V3427"  "V3428"  "V3429"  "V3430"  "V3431"  "V3432" 
##  [3433] "V3433"  "V3434"  "V3435"  "V3436"  "V3437"  "V3438"  "V3439"  "V3440" 
##  [3441] "V3441"  "V3442"  "V3443"  "V3444"  "V3445"  "V3446"  "V3447"  "V3448" 
##  [3449] "V3449"  "V3450"  "V3451"  "V3452"  "V3453"  "V3454"  "V3455"  "V3456" 
##  [3457] "V3457"  "V3458"  "V3459"  "V3460"  "V3461"  "V3462"  "V3463"  "V3464" 
##  [3465] "V3465"  "V3466"  "V3467"  "V3468"  "V3469"  "V3470"  "V3471"  "V3472" 
##  [3473] "V3473"  "V3474"  "V3475"  "V3476"  "V3477"  "V3478"  "V3479"  "V3480" 
##  [3481] "V3481"  "V3482"  "V3483"  "V3484"  "V3485"  "V3486"  "V3487"  "V3488" 
##  [3489] "V3489"  "V3490"  "V3491"  "V3492"  "V3493"  "V3494"  "V3495"  "V3496" 
##  [3497] "V3497"  "V3498"  "V3499"  "V3500"  "V3501"  "V3502"  "V3503"  "V3504" 
##  [3505] "V3505"  "V3506"  "V3507"  "V3508"  "V3509"  "V3510"  "V3511"  "V3512" 
##  [3513] "V3513"  "V3514"  "V3515"  "V3516"  "V3517"  "V3518"  "V3519"  "V3520" 
##  [3521] "V3521"  "V3522"  "V3523"  "V3524"  "V3525"  "V3526"  "V3527"  "V3528" 
##  [3529] "V3529"  "V3530"  "V3531"  "V3532"  "V3533"  "V3534"  "V3535"  "V3536" 
##  [3537] "V3537"  "V3538"  "V3539"  "V3540"  "V3541"  "V3542"  "V3543"  "V3544" 
##  [3545] "V3545"  "V3546"  "V3547"  "V3548"  "V3549"  "V3550"  "V3551"  "V3552" 
##  [3553] "V3553"  "V3554"  "V3555"  "V3556"  "V3557"  "V3558"  "V3559"  "V3560" 
##  [3561] "V3561"  "V3562"  "V3563"  "V3564"  "V3565"  "V3566"  "V3567"  "V3568" 
##  [3569] "V3569"  "V3570"  "V3571"  "V3572"  "V3573"  "V3574"  "V3575"  "V3576" 
##  [3577] "V3577"  "V3578"  "V3579"  "V3580"  "V3581"  "V3582"  "V3583"  "V3584" 
##  [3585] "V3585"  "V3586"  "V3587"  "V3588"  "V3589"  "V3590"  "V3591"  "V3592" 
##  [3593] "V3593"  "V3594"  "V3595"  "V3596"  "V3597"  "V3598"  "V3599"  "V3600" 
##  [3601] "V3601"  "V3602"  "V3603"  "V3604"  "V3605"  "V3606"  "V3607"  "V3608" 
##  [3609] "V3609"  "V3610"  "V3611"  "V3612"  "V3613"  "V3614"  "V3615"  "V3616" 
##  [3617] "V3617"  "V3618"  "V3619"  "V3620"  "V3621"  "V3622"  "V3623"  "V3624" 
##  [3625] "V3625"  "V3626"  "V3627"  "V3628"  "V3629"  "V3630"  "V3631"  "V3632" 
##  [3633] "V3633"  "V3634"  "V3635"  "V3636"  "V3637"  "V3638"  "V3639"  "V3640" 
##  [3641] "V3641"  "V3642"  "V3643"  "V3644"  "V3645"  "V3646"  "V3647"  "V3648" 
##  [3649] "V3649"  "V3650"  "V3651"  "V3652"  "V3653"  "V3654"  "V3655"  "V3656" 
##  [3657] "V3657"  "V3658"  "V3659"  "V3660"  "V3661"  "V3662"  "V3663"  "V3664" 
##  [3665] "V3665"  "V3666"  "V3667"  "V3668"  "V3669"  "V3670"  "V3671"  "V3672" 
##  [3673] "V3673"  "V3674"  "V3675"  "V3676"  "V3677"  "V3678"  "V3679"  "V3680" 
##  [3681] "V3681"  "V3682"  "V3683"  "V3684"  "V3685"  "V3686"  "V3687"  "V3688" 
##  [3689] "V3689"  "V3690"  "V3691"  "V3692"  "V3693"  "V3694"  "V3695"  "V3696" 
##  [3697] "V3697"  "V3698"  "V3699"  "V3700"  "V3701"  "V3702"  "V3703"  "V3704" 
##  [3705] "V3705"  "V3706"  "V3707"  "V3708"  "V3709"  "V3710"  "V3711"  "V3712" 
##  [3713] "V3713"  "V3714"  "V3715"  "V3716"  "V3717"  "V3718"  "V3719"  "V3720" 
##  [3721] "V3721"  "V3722"  "V3723"  "V3724"  "V3725"  "V3726"  "V3727"  "V3728" 
##  [3729] "V3729"  "V3730"  "V3731"  "V3732"  "V3733"  "V3734"  "V3735"  "V3736" 
##  [3737] "V3737"  "V3738"  "V3739"  "V3740"  "V3741"  "V3742"  "V3743"  "V3744" 
##  [3745] "V3745"  "V3746"  "V3747"  "V3748"  "V3749"  "V3750"  "V3751"  "V3752" 
##  [3753] "V3753"  "V3754"  "V3755"  "V3756"  "V3757"  "V3758"  "V3759"  "V3760" 
##  [3761] "V3761"  "V3762"  "V3763"  "V3764"  "V3765"  "V3766"  "V3767"  "V3768" 
##  [3769] "V3769"  "V3770"  "V3771"  "V3772"  "V3773"  "V3774"  "V3775"  "V3776" 
##  [3777] "V3777"  "V3778"  "V3779"  "V3780"  "V3781"  "V3782"  "V3783"  "V3784" 
##  [3785] "V3785"  "V3786"  "V3787"  "V3788"  "V3789"  "V3790"  "V3791"  "V3792" 
##  [3793] "V3793"  "V3794"  "V3795"  "V3796"  "V3797"  "V3798"  "V3799"  "V3800" 
##  [3801] "V3801"  "V3802"  "V3803"  "V3804"  "V3805"  "V3806"  "V3807"  "V3808" 
##  [3809] "V3809"  "V3810"  "V3811"  "V3812"  "V3813"  "V3814"  "V3815"  "V3816" 
##  [3817] "V3817"  "V3818"  "V3819"  "V3820"  "V3821"  "V3822"  "V3823"  "V3824" 
##  [3825] "V3825"  "V3826"  "V3827"  "V3828"  "V3829"  "V3830"  "V3831"  "V3832" 
##  [3833] "V3833"  "V3834"  "V3835"  "V3836"  "V3837"  "V3838"  "V3839"  "V3840" 
##  [3841] "V3841"  "V3842"  "V3843"  "V3844"  "V3845"  "V3846"  "V3847"  "V3848" 
##  [3849] "V3849"  "V3850"  "V3851"  "V3852"  "V3853"  "V3854"  "V3855"  "V3856" 
##  [3857] "V3857"  "V3858"  "V3859"  "V3860"  "V3861"  "V3862"  "V3863"  "V3864" 
##  [3865] "V3865"  "V3866"  "V3867"  "V3868"  "V3869"  "V3870"  "V3871"  "V3872" 
##  [3873] "V3873"  "V3874"  "V3875"  "V3876"  "V3877"  "V3878"  "V3879"  "V3880" 
##  [3881] "V3881"  "V3882"  "V3883"  "V3884"  "V3885"  "V3886"  "V3887"  "V3888" 
##  [3889] "V3889"  "V3890"  "V3891"  "V3892"  "V3893"  "V3894"  "V3895"  "V3896" 
##  [3897] "V3897"  "V3898"  "V3899"  "V3900"  "V3901"  "V3902"  "V3903"  "V3904" 
##  [3905] "V3905"  "V3906"  "V3907"  "V3908"  "V3909"  "V3910"  "V3911"  "V3912" 
##  [3913] "V3913"  "V3914"  "V3915"  "V3916"  "V3917"  "V3918"  "V3919"  "V3920" 
##  [3921] "V3921"  "V3922"  "V3923"  "V3924"  "V3925"  "V3926"  "V3927"  "V3928" 
##  [3929] "V3929"  "V3930"  "V3931"  "V3932"  "V3933"  "V3934"  "V3935"  "V3936" 
##  [3937] "V3937"  "V3938"  "V3939"  "V3940"  "V3941"  "V3942"  "V3943"  "V3944" 
##  [3945] "V3945"  "V3946"  "V3947"  "V3948"  "V3949"  "V3950"  "V3951"  "V3952" 
##  [3953] "V3953"  "V3954"  "V3955"  "V3956"  "V3957"  "V3958"  "V3959"  "V3960" 
##  [3961] "V3961"  "V3962"  "V3963"  "V3964"  "V3965"  "V3966"  "V3967"  "V3968" 
##  [3969] "V3969"  "V3970"  "V3971"  "V3972"  "V3973"  "V3974"  "V3975"  "V3976" 
##  [3977] "V3977"  "V3978"  "V3979"  "V3980"  "V3981"  "V3982"  "V3983"  "V3984" 
##  [3985] "V3985"  "V3986"  "V3987"  "V3988"  "V3989"  "V3990"  "V3991"  "V3992" 
##  [3993] "V3993"  "V3994"  "V3995"  "V3996"  "V3997"  "V3998"  "V3999"  "V4000" 
##  [4001] "V4001"  "V4002"  "V4003"  "V4004"  "V4005"  "V4006"  "V4007"  "V4008" 
##  [4009] "V4009"  "V4010"  "V4011"  "V4012"  "V4013"  "V4014"  "V4015"  "V4016" 
##  [4017] "V4017"  "V4018"  "V4019"  "V4020"  "V4021"  "V4022"  "V4023"  "V4024" 
##  [4025] "V4025"  "V4026"  "V4027"  "V4028"  "V4029"  "V4030"  "V4031"  "V4032" 
##  [4033] "V4033"  "V4034"  "V4035"  "V4036"  "V4037"  "V4038"  "V4039"  "V4040" 
##  [4041] "V4041"  "V4042"  "V4043"  "V4044"  "V4045"  "V4046"  "V4047"  "V4048" 
##  [4049] "V4049"  "V4050"  "V4051"  "V4052"  "V4053"  "V4054"  "V4055"  "V4056" 
##  [4057] "V4057"  "V4058"  "V4059"  "V4060"  "V4061"  "V4062"  "V4063"  "V4064" 
##  [4065] "V4065"  "V4066"  "V4067"  "V4068"  "V4069"  "V4070"  "V4071"  "V4072" 
##  [4073] "V4073"  "V4074"  "V4075"  "V4076"  "V4077"  "V4078"  "V4079"  "V4080" 
##  [4081] "V4081"  "V4082"  "V4083"  "V4084"  "V4085"  "V4086"  "V4087"  "V4088" 
##  [4089] "V4089"  "V4090"  "V4091"  "V4092"  "V4093"  "V4094"  "V4095"  "V4096" 
##  [4097] "V4097"  "V4098"  "V4099"  "V4100"  "V4101"  "V4102"  "V4103"  "V4104" 
##  [4105] "V4105"  "V4106"  "V4107"  "V4108"  "V4109"  "V4110"  "V4111"  "V4112" 
##  [4113] "V4113"  "V4114"  "V4115"  "V4116"  "V4117"  "V4118"  "V4119"  "V4120" 
##  [4121] "V4121"  "V4122"  "V4123"  "V4124"  "V4125"  "V4126"  "V4127"  "V4128" 
##  [4129] "V4129"  "V4130"  "V4131"  "V4132"  "V4133"  "V4134"  "V4135"  "V4136" 
##  [4137] "V4137"  "V4138"  "V4139"  "V4140"  "V4141"  "V4142"  "V4143"  "V4144" 
##  [4145] "V4145"  "V4146"  "V4147"  "V4148"  "V4149"  "V4150"  "V4151"  "V4152" 
##  [4153] "V4153"  "V4154"  "V4155"  "V4156"  "V4157"  "V4158"  "V4159"  "V4160" 
##  [4161] "V4161"  "V4162"  "V4163"  "V4164"  "V4165"  "V4166"  "V4167"  "V4168" 
##  [4169] "V4169"  "V4170"  "V4171"  "V4172"  "V4173"  "V4174"  "V4175"  "V4176" 
##  [4177] "V4177"  "V4178"  "V4179"  "V4180"  "V4181"  "V4182"  "V4183"  "V4184" 
##  [4185] "V4185"  "V4186"  "V4187"  "V4188"  "V4189"  "V4190"  "V4191"  "V4192" 
##  [4193] "V4193"  "V4194"  "V4195"  "V4196"  "V4197"  "V4198"  "V4199"  "V4200" 
##  [4201] "V4201"  "V4202"  "V4203"  "V4204"  "V4205"  "V4206"  "V4207"  "V4208" 
##  [4209] "V4209"  "V4210"  "V4211"  "V4212"  "V4213"  "V4214"  "V4215"  "V4216" 
##  [4217] "V4217"  "V4218"  "V4219"  "V4220"  "V4221"  "V4222"  "V4223"  "V4224" 
##  [4225] "V4225"  "V4226"  "V4227"  "V4228"  "V4229"  "V4230"  "V4231"  "V4232" 
##  [4233] "V4233"  "V4234"  "V4235"  "V4236"  "V4237"  "V4238"  "V4239"  "V4240" 
##  [4241] "V4241"  "V4242"  "V4243"  "V4244"  "V4245"  "V4246"  "V4247"  "V4248" 
##  [4249] "V4249"  "V4250"  "V4251"  "V4252"  "V4253"  "V4254"  "V4255"  "V4256" 
##  [4257] "V4257"  "V4258"  "V4259"  "V4260"  "V4261"  "V4262"  "V4263"  "V4264" 
##  [4265] "V4265"  "V4266"  "V4267"  "V4268"  "V4269"  "V4270"  "V4271"  "V4272" 
##  [4273] "V4273"  "V4274"  "V4275"  "V4276"  "V4277"  "V4278"  "V4279"  "V4280" 
##  [4281] "V4281"  "V4282"  "V4283"  "V4284"  "V4285"  "V4286"  "V4287"  "V4288" 
##  [4289] "V4289"  "V4290"  "V4291"  "V4292"  "V4293"  "V4294"  "V4295"  "V4296" 
##  [4297] "V4297"  "V4298"  "V4299"  "V4300"  "V4301"  "V4302"  "V4303"  "V4304" 
##  [4305] "V4305"  "V4306"  "V4307"  "V4308"  "V4309"  "V4310"  "V4311"  "V4312" 
##  [4313] "V4313"  "V4314"  "V4315"  "V4316"  "V4317"  "V4318"  "V4319"  "V4320" 
##  [4321] "V4321"  "V4322"  "V4323"  "V4324"  "V4325"  "V4326"  "V4327"  "V4328" 
##  [4329] "V4329"  "V4330"  "V4331"  "V4332"  "V4333"  "V4334"  "V4335"  "V4336" 
##  [4337] "V4337"  "V4338"  "V4339"  "V4340"  "V4341"  "V4342"  "V4343"  "V4344" 
##  [4345] "V4345"  "V4346"  "V4347"  "V4348"  "V4349"  "V4350"  "V4351"  "V4352" 
##  [4353] "V4353"  "V4354"  "V4355"  "V4356"  "V4357"  "V4358"  "V4359"  "V4360" 
##  [4361] "V4361"  "V4362"  "V4363"  "V4364"  "V4365"  "V4366"  "V4367"  "V4368" 
##  [4369] "V4369"  "V4370"  "V4371"  "V4372"  "V4373"  "V4374"  "V4375"  "V4376" 
##  [4377] "V4377"  "V4378"  "V4379"  "V4380"  "V4381"  "V4382"  "V4383"  "V4384" 
##  [4385] "V4385"  "V4386"  "V4387"  "V4388"  "V4389"  "V4390"  "V4391"  "V4392" 
##  [4393] "V4393"  "V4394"  "V4395"  "V4396"  "V4397"  "V4398"  "V4399"  "V4400" 
##  [4401] "V4401"  "V4402"  "V4403"  "V4404"  "V4405"  "V4406"  "V4407"  "V4408" 
##  [4409] "V4409"  "V4410"  "V4411"  "V4412"  "V4413"  "V4414"  "V4415"  "V4416" 
##  [4417] "V4417"  "V4418"  "V4419"  "V4420"  "V4421"  "V4422"  "V4423"  "V4424" 
##  [4425] "V4425"  "V4426"  "V4427"  "V4428"  "V4429"  "V4430"  "V4431"  "V4432" 
##  [4433] "V4433"  "V4434"  "V4435"  "V4436"  "V4437"  "V4438"  "V4439"  "V4440" 
##  [4441] "V4441"  "V4442"  "V4443"  "V4444"  "V4445"  "V4446"  "V4447"  "V4448" 
##  [4449] "V4449"  "V4450"  "V4451"  "V4452"  "V4453"  "V4454"  "V4455"  "V4456" 
##  [4457] "V4457"  "V4458"  "V4459"  "V4460"  "V4461"  "V4462"  "V4463"  "V4464" 
##  [4465] "V4465"  "V4466"  "V4467"  "V4468"  "V4469"  "V4470"  "V4471"  "V4472" 
##  [4473] "V4473"  "V4474"  "V4475"  "V4476"  "V4477"  "V4478"  "V4479"  "V4480" 
##  [4481] "V4481"  "V4482"  "V4483"  "V4484"  "V4485"  "V4486"  "V4487"  "V4488" 
##  [4489] "V4489"  "V4490"  "V4491"  "V4492"  "V4493"  "V4494"  "V4495"  "V4496" 
##  [4497] "V4497"  "V4498"  "V4499"  "V4500"  "V4501"  "V4502"  "V4503"  "V4504" 
##  [4505] "V4505"  "V4506"  "V4507"  "V4508"  "V4509"  "V4510"  "V4511"  "V4512" 
##  [4513] "V4513"  "V4514"  "V4515"  "V4516"  "V4517"  "V4518"  "V4519"  "V4520" 
##  [4521] "V4521"  "V4522"  "V4523"  "V4524"  "V4525"  "V4526"  "V4527"  "V4528" 
##  [4529] "V4529"  "V4530"  "V4531"  "V4532"  "V4533"  "V4534"  "V4535"  "V4536" 
##  [4537] "V4537"  "V4538"  "V4539"  "V4540"  "V4541"  "V4542"  "V4543"  "V4544" 
##  [4545] "V4545"  "V4546"  "V4547"  "V4548"  "V4549"  "V4550"  "V4551"  "V4552" 
##  [4553] "V4553"  "V4554"  "V4555"  "V4556"  "V4557"  "V4558"  "V4559"  "V4560" 
##  [4561] "V4561"  "V4562"  "V4563"  "V4564"  "V4565"  "V4566"  "V4567"  "V4568" 
##  [4569] "V4569"  "V4570"  "V4571"  "V4572"  "V4573"  "V4574"  "V4575"  "V4576" 
##  [4577] "V4577"  "V4578"  "V4579"  "V4580"  "V4581"  "V4582"  "V4583"  "V4584" 
##  [4585] "V4585"  "V4586"  "V4587"  "V4588"  "V4589"  "V4590"  "V4591"  "V4592" 
##  [4593] "V4593"  "V4594"  "V4595"  "V4596"  "V4597"  "V4598"  "V4599"  "V4600" 
##  [4601] "V4601"  "V4602"  "V4603"  "V4604"  "V4605"  "V4606"  "V4607"  "V4608" 
##  [4609] "V4609"  "V4610"  "V4611"  "V4612"  "V4613"  "V4614"  "V4615"  "V4616" 
##  [4617] "V4617"  "V4618"  "V4619"  "V4620"  "V4621"  "V4622"  "V4623"  "V4624" 
##  [4625] "V4625"  "V4626"  "V4627"  "V4628"  "V4629"  "V4630"  "V4631"  "V4632" 
##  [4633] "V4633"  "V4634"  "V4635"  "V4636"  "V4637"  "V4638"  "V4639"  "V4640" 
##  [4641] "V4641"  "V4642"  "V4643"  "V4644"  "V4645"  "V4646"  "V4647"  "V4648" 
##  [4649] "V4649"  "V4650"  "V4651"  "V4652"  "V4653"  "V4654"  "V4655"  "V4656" 
##  [4657] "V4657"  "V4658"  "V4659"  "V4660"  "V4661"  "V4662"  "V4663"  "V4664" 
##  [4665] "V4665"  "V4666"  "V4667"  "V4668"  "V4669"  "V4670"  "V4671"  "V4672" 
##  [4673] "V4673"  "V4674"  "V4675"  "V4676"  "V4677"  "V4678"  "V4679"  "V4680" 
##  [4681] "V4681"  "V4682"  "V4683"  "V4684"  "V4685"  "V4686"  "V4687"  "V4688" 
##  [4689] "V4689"  "V4690"  "V4691"  "V4692"  "V4693"  "V4694"  "V4695"  "V4696" 
##  [4697] "V4697"  "V4698"  "V4699"  "V4700"  "V4701"  "V4702"  "V4703"  "V4704" 
##  [4705] "V4705"  "V4706"  "V4707"  "V4708"  "V4709"  "V4710"  "V4711"  "V4712" 
##  [4713] "V4713"  "V4714"  "V4715"  "V4716"  "V4717"  "V4718"  "V4719"  "V4720" 
##  [4721] "V4721"  "V4722"  "V4723"  "V4724"  "V4725"  "V4726"  "V4727"  "V4728" 
##  [4729] "V4729"  "V4730"  "V4731"  "V4732"  "V4733"  "V4734"  "V4735"  "V4736" 
##  [4737] "V4737"  "V4738"  "V4739"  "V4740"  "V4741"  "V4742"  "V4743"  "V4744" 
##  [4745] "V4745"  "V4746"  "V4747"  "V4748"  "V4749"  "V4750"  "V4751"  "V4752" 
##  [4753] "V4753"  "V4754"  "V4755"  "V4756"  "V4757"  "V4758"  "V4759"  "V4760" 
##  [4761] "V4761"  "V4762"  "V4763"  "V4764"  "V4765"  "V4766"  "V4767"  "V4768" 
##  [4769] "V4769"  "V4770"  "V4771"  "V4772"  "V4773"  "V4774"  "V4775"  "V4776" 
##  [4777] "V4777"  "V4778"  "V4779"  "V4780"  "V4781"  "V4782"  "V4783"  "V4784" 
##  [4785] "V4785"  "V4786"  "V4787"  "V4788"  "V4789"  "V4790"  "V4791"  "V4792" 
##  [4793] "V4793"  "V4794"  "V4795"  "V4796"  "V4797"  "V4798"  "V4799"  "V4800" 
##  [4801] "V4801"  "V4802"  "V4803"  "V4804"  "V4805"  "V4806"  "V4807"  "V4808" 
##  [4809] "V4809"  "V4810"  "V4811"  "V4812"  "V4813"  "V4814"  "V4815"  "V4816" 
##  [4817] "V4817"  "V4818"  "V4819"  "V4820"  "V4821"  "V4822"  "V4823"  "V4824" 
##  [4825] "V4825"  "V4826"  "V4827"  "V4828"  "V4829"  "V4830"  "V4831"  "V4832" 
##  [4833] "V4833"  "V4834"  "V4835"  "V4836"  "V4837"  "V4838"  "V4839"  "V4840" 
##  [4841] "V4841"  "V4842"  "V4843"  "V4844"  "V4845"  "V4846"  "V4847"  "V4848" 
##  [4849] "V4849"  "V4850"  "V4851"  "V4852"  "V4853"  "V4854"  "V4855"  "V4856" 
##  [4857] "V4857"  "V4858"  "V4859"  "V4860"  "V4861"  "V4862"  "V4863"  "V4864" 
##  [4865] "V4865"  "V4866"  "V4867"  "V4868"  "V4869"  "V4870"  "V4871"  "V4872" 
##  [4873] "V4873"  "V4874"  "V4875"  "V4876"  "V4877"  "V4878"  "V4879"  "V4880" 
##  [4881] "V4881"  "V4882"  "V4883"  "V4884"  "V4885"  "V4886"  "V4887"  "V4888" 
##  [4889] "V4889"  "V4890"  "V4891"  "V4892"  "V4893"  "V4894"  "V4895"  "V4896" 
##  [4897] "V4897"  "V4898"  "V4899"  "V4900"  "V4901"  "V4902"  "V4903"  "V4904" 
##  [4905] "V4905"  "V4906"  "V4907"  "V4908"  "V4909"  "V4910"  "V4911"  "V4912" 
##  [4913] "V4913"  "V4914"  "V4915"  "V4916"  "V4917"  "V4918"  "V4919"  "V4920" 
##  [4921] "V4921"  "V4922"  "V4923"  "V4924"  "V4925"  "V4926"  "V4927"  "V4928" 
##  [4929] "V4929"  "V4930"  "V4931"  "V4932"  "V4933"  "V4934"  "V4935"  "V4936" 
##  [4937] "V4937"  "V4938"  "V4939"  "V4940"  "V4941"  "V4942"  "V4943"  "V4944" 
##  [4945] "V4945"  "V4946"  "V4947"  "V4948"  "V4949"  "V4950"  "V4951"  "V4952" 
##  [4953] "V4953"  "V4954"  "V4955"  "V4956"  "V4957"  "V4958"  "V4959"  "V4960" 
##  [4961] "V4961"  "V4962"  "V4963"  "V4964"  "V4965"  "V4966"  "V4967"  "V4968" 
##  [4969] "V4969"  "V4970"  "V4971"  "V4972"  "V4973"  "V4974"  "V4975"  "V4976" 
##  [4977] "V4977"  "V4978"  "V4979"  "V4980"  "V4981"  "V4982"  "V4983"  "V4984" 
##  [4985] "V4985"  "V4986"  "V4987"  "V4988"  "V4989"  "V4990"  "V4991"  "V4992" 
##  [4993] "V4993"  "V4994"  "V4995"  "V4996"  "V4997"  "V4998"  "V4999"  "V5000" 
##  [5001] "V5001"  "V5002"  "V5003"  "V5004"  "V5005"  "V5006"  "V5007"  "V5008" 
##  [5009] "V5009"  "V5010"  "V5011"  "V5012"  "V5013"  "V5014"  "V5015"  "V5016" 
##  [5017] "V5017"  "V5018"  "V5019"  "V5020"  "V5021"  "V5022"  "V5023"  "V5024" 
##  [5025] "V5025"  "V5026"  "V5027"  "V5028"  "V5029"  "V5030"  "V5031"  "V5032" 
##  [5033] "V5033"  "V5034"  "V5035"  "V5036"  "V5037"  "V5038"  "V5039"  "V5040" 
##  [5041] "V5041"  "V5042"  "V5043"  "V5044"  "V5045"  "V5046"  "V5047"  "V5048" 
##  [5049] "V5049"  "V5050"  "V5051"  "V5052"  "V5053"  "V5054"  "V5055"  "V5056" 
##  [5057] "V5057"  "V5058"  "V5059"  "V5060"  "V5061"  "V5062"  "V5063"  "V5064" 
##  [5065] "V5065"  "V5066"  "V5067"  "V5068"  "V5069"  "V5070"  "V5071"  "V5072" 
##  [5073] "V5073"  "V5074"  "V5075"  "V5076"  "V5077"  "V5078"  "V5079"  "V5080" 
##  [5081] "V5081"  "V5082"  "V5083"  "V5084"  "V5085"  "V5086"  "V5087"  "V5088" 
##  [5089] "V5089"  "V5090"  "V5091"  "V5092"  "V5093"  "V5094"  "V5095"  "V5096" 
##  [5097] "V5097"  "V5098"  "V5099"  "V5100"  "V5101"  "V5102"  "V5103"  "V5104" 
##  [5105] "V5105"  "V5106"  "V5107"  "V5108"  "V5109"  "V5110"  "V5111"  "V5112" 
##  [5113] "V5113"  "V5114"  "V5115"  "V5116"  "V5117"  "V5118"  "V5119"  "V5120" 
##  [5121] "V5121"  "V5122"  "V5123"  "V5124"  "V5125"  "V5126"  "V5127"  "V5128" 
##  [5129] "V5129"  "V5130"  "V5131"  "V5132"  "V5133"  "V5134"  "V5135"  "V5136" 
##  [5137] "V5137"  "V5138"  "V5139"  "V5140"  "V5141"  "V5142"  "V5143"  "V5144" 
##  [5145] "V5145"  "V5146"  "V5147"  "V5148"  "V5149"  "V5150"  "V5151"  "V5152" 
##  [5153] "V5153"  "V5154"  "V5155"  "V5156"  "V5157"  "V5158"  "V5159"  "V5160" 
##  [5161] "V5161"  "V5162"  "V5163"  "V5164"  "V5165"  "V5166"  "V5167"  "V5168" 
##  [5169] "V5169"  "V5170"  "V5171"  "V5172"  "V5173"  "V5174"  "V5175"  "V5176" 
##  [5177] "V5177"  "V5178"  "V5179"  "V5180"  "V5181"  "V5182"  "V5183"  "V5184" 
##  [5185] "V5185"  "V5186"  "V5187"  "V5188"  "V5189"  "V5190"  "V5191"  "V5192" 
##  [5193] "V5193"  "V5194"  "V5195"  "V5196"  "V5197"  "V5198"  "V5199"  "V5200" 
##  [5201] "V5201"  "V5202"  "V5203"  "V5204"  "V5205"  "V5206"  "V5207"  "V5208" 
##  [5209] "V5209"  "V5210"  "V5211"  "V5212"  "V5213"  "V5214"  "V5215"  "V5216" 
##  [5217] "V5217"  "V5218"  "V5219"  "V5220"  "V5221"  "V5222"  "V5223"  "V5224" 
##  [5225] "V5225"  "V5226"  "V5227"  "V5228"  "V5229"  "V5230"  "V5231"  "V5232" 
##  [5233] "V5233"  "V5234"  "V5235"  "V5236"  "V5237"  "V5238"  "V5239"  "V5240" 
##  [5241] "V5241"  "V5242"  "V5243"  "V5244"  "V5245"  "V5246"  "V5247"  "V5248" 
##  [5249] "V5249"  "V5250"  "V5251"  "V5252"  "V5253"  "V5254"  "V5255"  "V5256" 
##  [5257] "V5257"  "V5258"  "V5259"  "V5260"  "V5261"  "V5262"  "V5263"  "V5264" 
##  [5265] "V5265"  "V5266"  "V5267"  "V5268"  "V5269"  "V5270"  "V5271"  "V5272" 
##  [5273] "V5273"  "V5274"  "V5275"  "V5276"  "V5277"  "V5278"  "V5279"  "V5280" 
##  [5281] "V5281"  "V5282"  "V5283"  "V5284"  "V5285"  "V5286"  "V5287"  "V5288" 
##  [5289] "V5289"  "V5290"  "V5291"  "V5292"  "V5293"  "V5294"  "V5295"  "V5296" 
##  [5297] "V5297"  "V5298"  "V5299"  "V5300"  "V5301"  "V5302"  "V5303"  "V5304" 
##  [5305] "V5305"  "V5306"  "V5307"  "V5308"  "V5309"  "V5310"  "V5311"  "V5312" 
##  [5313] "V5313"  "V5314"  "V5315"  "V5316"  "V5317"  "V5318"  "V5319"  "V5320" 
##  [5321] "V5321"  "V5322"  "V5323"  "V5324"  "V5325"  "V5326"  "V5327"  "V5328" 
##  [5329] "V5329"  "V5330"  "V5331"  "V5332"  "V5333"  "V5334"  "V5335"  "V5336" 
##  [5337] "V5337"  "V5338"  "V5339"  "V5340"  "V5341"  "V5342"  "V5343"  "V5344" 
##  [5345] "V5345"  "V5346"  "V5347"  "V5348"  "V5349"  "V5350"  "V5351"  "V5352" 
##  [5353] "V5353"  "V5354"  "V5355"  "V5356"  "V5357"  "V5358"  "V5359"  "V5360" 
##  [5361] "V5361"  "V5362"  "V5363"  "V5364"  "V5365"  "V5366"  "V5367"  "V5368" 
##  [5369] "V5369"  "V5370"  "V5371"  "V5372"  "V5373"  "V5374"  "V5375"  "V5376" 
##  [5377] "V5377"  "V5378"  "V5379"  "V5380"  "V5381"  "V5382"  "V5383"  "V5384" 
##  [5385] "V5385"  "V5386"  "V5387"  "V5388"  "V5389"  "V5390"  "V5391"  "V5392" 
##  [5393] "V5393"  "V5394"  "V5395"  "V5396"  "V5397"  "V5398"  "V5399"  "V5400" 
##  [5401] "V5401"  "V5402"  "V5403"  "V5404"  "V5405"  "V5406"  "V5407"  "V5408" 
##  [5409] "V5409"  "V5410"  "V5411"  "V5412"  "V5413"  "V5414"  "V5415"  "V5416" 
##  [5417] "V5417"  "V5418"  "V5419"  "V5420"  "V5421"  "V5422"  "V5423"  "V5424" 
##  [5425] "V5425"  "V5426"  "V5427"  "V5428"  "V5429"  "V5430"  "V5431"  "V5432" 
##  [5433] "V5433"  "V5434"  "V5435"  "V5436"  "V5437"  "V5438"  "V5439"  "V5440" 
##  [5441] "V5441"  "V5442"  "V5443"  "V5444"  "V5445"  "V5446"  "V5447"  "V5448" 
##  [5449] "V5449"  "V5450"  "V5451"  "V5452"  "V5453"  "V5454"  "V5455"  "V5456" 
##  [5457] "V5457"  "V5458"  "V5459"  "V5460"  "V5461"  "V5462"  "V5463"  "V5464" 
##  [5465] "V5465"  "V5466"  "V5467"  "V5468"  "V5469"  "V5470"  "V5471"  "V5472" 
##  [5473] "V5473"  "V5474"  "V5475"  "V5476"  "V5477"  "V5478"  "V5479"  "V5480" 
##  [5481] "V5481"  "V5482"  "V5483"  "V5484"  "V5485"  "V5486"  "V5487"  "V5488" 
##  [5489] "V5489"  "V5490"  "V5491"  "V5492"  "V5493"  "V5494"  "V5495"  "V5496" 
##  [5497] "V5497"  "V5498"  "V5499"  "V5500"  "V5501"  "V5502"  "V5503"  "V5504" 
##  [5505] "V5505"  "V5506"  "V5507"  "V5508"  "V5509"  "V5510"  "V5511"  "V5512" 
##  [5513] "V5513"  "V5514"  "V5515"  "V5516"  "V5517"  "V5518"  "V5519"  "V5520" 
##  [5521] "V5521"  "V5522"  "V5523"  "V5524"  "V5525"  "V5526"  "V5527"  "V5528" 
##  [5529] "V5529"  "V5530"  "V5531"  "V5532"  "V5533"  "V5534"  "V5535"  "V5536" 
##  [5537] "V5537"  "V5538"  "V5539"  "V5540"  "V5541"  "V5542"  "V5543"  "V5544" 
##  [5545] "V5545"  "V5546"  "V5547"  "V5548"  "V5549"  "V5550"  "V5551"  "V5552" 
##  [5553] "V5553"  "V5554"  "V5555"  "V5556"  "V5557"  "V5558"  "V5559"  "V5560" 
##  [5561] "V5561"  "V5562"  "V5563"  "V5564"  "V5565"  "V5566"  "V5567"  "V5568" 
##  [5569] "V5569"  "V5570"  "V5571"  "V5572"  "V5573"  "V5574"  "V5575"  "V5576" 
##  [5577] "V5577"  "V5578"  "V5579"  "V5580"  "V5581"  "V5582"  "V5583"  "V5584" 
##  [5585] "V5585"  "V5586"  "V5587"  "V5588"  "V5589"  "V5590"  "V5591"  "V5592" 
##  [5593] "V5593"  "V5594"  "V5595"  "V5596"  "V5597"  "V5598"  "V5599"  "V5600" 
##  [5601] "V5601"  "V5602"  "V5603"  "V5604"  "V5605"  "V5606"  "V5607"  "V5608" 
##  [5609] "V5609"  "V5610"  "V5611"  "V5612"  "V5613"  "V5614"  "V5615"  "V5616" 
##  [5617] "V5617"  "V5618"  "V5619"  "V5620"  "V5621"  "V5622"  "V5623"  "V5624" 
##  [5625] "V5625"  "V5626"  "V5627"  "V5628"  "V5629"  "V5630"  "V5631"  "V5632" 
##  [5633] "V5633"  "V5634"  "V5635"  "V5636"  "V5637"  "V5638"  "V5639"  "V5640" 
##  [5641] "V5641"  "V5642"  "V5643"  "V5644"  "V5645"  "V5646"  "V5647"  "V5648" 
##  [5649] "V5649"  "V5650"  "V5651"  "V5652"  "V5653"  "V5654"  "V5655"  "V5656" 
##  [5657] "V5657"  "V5658"  "V5659"  "V5660"  "V5661"  "V5662"  "V5663"  "V5664" 
##  [5665] "V5665"  "V5666"  "V5667"  "V5668"  "V5669"  "V5670"  "V5671"  "V5672" 
##  [5673] "V5673"  "V5674"  "V5675"  "V5676"  "V5677"  "V5678"  "V5679"  "V5680" 
##  [5681] "V5681"  "V5682"  "V5683"  "V5684"  "V5685"  "V5686"  "V5687"  "V5688" 
##  [5689] "V5689"  "V5690"  "V5691"  "V5692"  "V5693"  "V5694"  "V5695"  "V5696" 
##  [5697] "V5697"  "V5698"  "V5699"  "V5700"  "V5701"  "V5702"  "V5703"  "V5704" 
##  [5705] "V5705"  "V5706"  "V5707"  "V5708"  "V5709"  "V5710"  "V5711"  "V5712" 
##  [5713] "V5713"  "V5714"  "V5715"  "V5716"  "V5717"  "V5718"  "V5719"  "V5720" 
##  [5721] "V5721"  "V5722"  "V5723"  "V5724"  "V5725"  "V5726"  "V5727"  "V5728" 
##  [5729] "V5729"  "V5730"  "V5731"  "V5732"  "V5733"  "V5734"  "V5735"  "V5736" 
##  [5737] "V5737"  "V5738"  "V5739"  "V5740"  "V5741"  "V5742"  "V5743"  "V5744" 
##  [5745] "V5745"  "V5746"  "V5747"  "V5748"  "V5749"  "V5750"  "V5751"  "V5752" 
##  [5753] "V5753"  "V5754"  "V5755"  "V5756"  "V5757"  "V5758"  "V5759"  "V5760" 
##  [5761] "V5761"  "V5762"  "V5763"  "V5764"  "V5765"  "V5766"  "V5767"  "V5768" 
##  [5769] "V5769"  "V5770"  "V5771"  "V5772"  "V5773"  "V5774"  "V5775"  "V5776" 
##  [5777] "V5777"  "V5778"  "V5779"  "V5780"  "V5781"  "V5782"  "V5783"  "V5784" 
##  [5785] "V5785"  "V5786"  "V5787"  "V5788"  "V5789"  "V5790"  "V5791"  "V5792" 
##  [5793] "V5793"  "V5794"  "V5795"  "V5796"  "V5797"  "V5798"  "V5799"  "V5800" 
##  [5801] "V5801"  "V5802"  "V5803"  "V5804"  "V5805"  "V5806"  "V5807"  "V5808" 
##  [5809] "V5809"  "V5810"  "V5811"  "V5812"  "V5813"  "V5814"  "V5815"  "V5816" 
##  [5817] "V5817"  "V5818"  "V5819"  "V5820"  "V5821"  "V5822"  "V5823"  "V5824" 
##  [5825] "V5825"  "V5826"  "V5827"  "V5828"  "V5829"  "V5830"  "V5831"  "V5832" 
##  [5833] "V5833"  "V5834"  "V5835"  "V5836"  "V5837"  "V5838"  "V5839"  "V5840" 
##  [5841] "V5841"  "V5842"  "V5843"  "V5844"  "V5845"  "V5846"  "V5847"  "V5848" 
##  [5849] "V5849"  "V5850"  "V5851"  "V5852"  "V5853"  "V5854"  "V5855"  "V5856" 
##  [5857] "V5857"  "V5858"  "V5859"  "V5860"  "V5861"  "V5862"  "V5863"  "V5864" 
##  [5865] "V5865"  "V5866"  "V5867"  "V5868"  "V5869"  "V5870"  "V5871"  "V5872" 
##  [5873] "V5873"  "V5874"  "V5875"  "V5876"  "V5877"  "V5878"  "V5879"  "V5880" 
##  [5881] "V5881"  "V5882"  "V5883"  "V5884"  "V5885"  "V5886"  "V5887"  "V5888" 
##  [5889] "V5889"  "V5890"  "V5891"  "V5892"  "V5893"  "V5894"  "V5895"  "V5896" 
##  [5897] "V5897"  "V5898"  "V5899"  "V5900"  "V5901"  "V5902"  "V5903"  "V5904" 
##  [5905] "V5905"  "V5906"  "V5907"  "V5908"  "V5909"  "V5910"  "V5911"  "V5912" 
##  [5913] "V5913"  "V5914"  "V5915"  "V5916"  "V5917"  "V5918"  "V5919"  "V5920" 
##  [5921] "V5921"  "V5922"  "V5923"  "V5924"  "V5925"  "V5926"  "V5927"  "V5928" 
##  [5929] "V5929"  "V5930"  "V5931"  "V5932"  "V5933"  "V5934"  "V5935"  "V5936" 
##  [5937] "V5937"  "V5938"  "V5939"  "V5940"  "V5941"  "V5942"  "V5943"  "V5944" 
##  [5945] "V5945"  "V5946"  "V5947"  "V5948"  "V5949"  "V5950"  "V5951"  "V5952" 
##  [5953] "V5953"  "V5954"  "V5955"  "V5956"  "V5957"  "V5958"  "V5959"  "V5960" 
##  [5961] "V5961"  "V5962"  "V5963"  "V5964"  "V5965"  "V5966"  "V5967"  "V5968" 
##  [5969] "V5969"  "V5970"  "V5971"  "V5972"  "V5973"  "V5974"  "V5975"  "V5976" 
##  [5977] "V5977"  "V5978"  "V5979"  "V5980"  "V5981"  "V5982"  "V5983"  "V5984" 
##  [5985] "V5985"  "V5986"  "V5987"  "V5988"  "V5989"  "V5990"  "V5991"  "V5992" 
##  [5993] "V5993"  "V5994"  "V5995"  "V5996"  "V5997"  "V5998"  "V5999"  "V6000" 
##  [6001] "V6001"  "V6002"  "V6003"  "V6004"  "V6005"  "V6006"  "V6007"  "V6008" 
##  [6009] "V6009"  "V6010"  "V6011"  "V6012"  "V6013"  "V6014"  "V6015"  "V6016" 
##  [6017] "V6017"  "V6018"  "V6019"  "V6020"  "V6021"  "V6022"  "V6023"  "V6024" 
##  [6025] "V6025"  "V6026"  "V6027"  "V6028"  "V6029"  "V6030"  "V6031"  "V6032" 
##  [6033] "V6033"  "V6034"  "V6035"  "V6036"  "V6037"  "V6038"  "V6039"  "V6040" 
##  [6041] "V6041"  "V6042"  "V6043"  "V6044"  "V6045"  "V6046"  "V6047"  "V6048" 
##  [6049] "V6049"  "V6050"  "V6051"  "V6052"  "V6053"  "V6054"  "V6055"  "V6056" 
##  [6057] "V6057"  "V6058"  "V6059"  "V6060"  "V6061"  "V6062"  "V6063"  "V6064" 
##  [6065] "V6065"  "V6066"  "V6067"  "V6068"  "V6069"  "V6070"  "V6071"  "V6072" 
##  [6073] "V6073"  "V6074"  "V6075"  "V6076"  "V6077"  "V6078"  "V6079"  "V6080" 
##  [6081] "V6081"  "V6082"  "V6083"  "V6084"  "V6085"  "V6086"  "V6087"  "V6088" 
##  [6089] "V6089"  "V6090"  "V6091"  "V6092"  "V6093"  "V6094"  "V6095"  "V6096" 
##  [6097] "V6097"  "V6098"  "V6099"  "V6100"  "V6101"  "V6102"  "V6103"  "V6104" 
##  [6105] "V6105"  "V6106"  "V6107"  "V6108"  "V6109"  "V6110"  "V6111"  "V6112" 
##  [6113] "V6113"  "V6114"  "V6115"  "V6116"  "V6117"  "V6118"  "V6119"  "V6120" 
##  [6121] "V6121"  "V6122"  "V6123"  "V6124"  "V6125"  "V6126"  "V6127"  "V6128" 
##  [6129] "V6129"  "V6130"  "V6131"  "V6132"  "V6133"  "V6134"  "V6135"  "V6136" 
##  [6137] "V6137"  "V6138"  "V6139"  "V6140"  "V6141"  "V6142"  "V6143"  "V6144" 
##  [6145] "V6145"  "V6146"  "V6147"  "V6148"  "V6149"  "V6150"  "V6151"  "V6152" 
##  [6153] "V6153"  "V6154"  "V6155"  "V6156"  "V6157"  "V6158"  "V6159"  "V6160" 
##  [6161] "V6161"  "V6162"  "V6163"  "V6164"  "V6165"  "V6166"  "V6167"  "V6168" 
##  [6169] "V6169"  "V6170"  "V6171"  "V6172"  "V6173"  "V6174"  "V6175"  "V6176" 
##  [6177] "V6177"  "V6178"  "V6179"  "V6180"  "V6181"  "V6182"  "V6183"  "V6184" 
##  [6185] "V6185"  "V6186"  "V6187"  "V6188"  "V6189"  "V6190"  "V6191"  "V6192" 
##  [6193] "V6193"  "V6194"  "V6195"  "V6196"  "V6197"  "V6198"  "V6199"  "V6200" 
##  [6201] "V6201"  "V6202"  "V6203"  "V6204"  "V6205"  "V6206"  "V6207"  "V6208" 
##  [6209] "V6209"  "V6210"  "V6211"  "V6212"  "V6213"  "V6214"  "V6215"  "V6216" 
##  [6217] "V6217"  "V6218"  "V6219"  "V6220"  "V6221"  "V6222"  "V6223"  "V6224" 
##  [6225] "V6225"  "V6226"  "V6227"  "V6228"  "V6229"  "V6230"  "V6231"  "V6232" 
##  [6233] "V6233"  "V6234"  "V6235"  "V6236"  "V6237"  "V6238"  "V6239"  "V6240" 
##  [6241] "V6241"  "V6242"  "V6243"  "V6244"  "V6245"  "V6246"  "V6247"  "V6248" 
##  [6249] "V6249"  "V6250"  "V6251"  "V6252"  "V6253"  "V6254"  "V6255"  "V6256" 
##  [6257] "V6257"  "V6258"  "V6259"  "V6260"  "V6261"  "V6262"  "V6263"  "V6264" 
##  [6265] "V6265"  "V6266"  "V6267"  "V6268"  "V6269"  "V6270"  "V6271"  "V6272" 
##  [6273] "V6273"  "V6274"  "V6275"  "V6276"  "V6277"  "V6278"  "V6279"  "V6280" 
##  [6281] "V6281"  "V6282"  "V6283"  "V6284"  "V6285"  "V6286"  "V6287"  "V6288" 
##  [6289] "V6289"  "V6290"  "V6291"  "V6292"  "V6293"  "V6294"  "V6295"  "V6296" 
##  [6297] "V6297"  "V6298"  "V6299"  "V6300"  "V6301"  "V6302"  "V6303"  "V6304" 
##  [6305] "V6305"  "V6306"  "V6307"  "V6308"  "V6309"  "V6310"  "V6311"  "V6312" 
##  [6313] "V6313"  "V6314"  "V6315"  "V6316"  "V6317"  "V6318"  "V6319"  "V6320" 
##  [6321] "V6321"  "V6322"  "V6323"  "V6324"  "V6325"  "V6326"  "V6327"  "V6328" 
##  [6329] "V6329"  "V6330"  "V6331"  "V6332"  "V6333"  "V6334"  "V6335"  "V6336" 
##  [6337] "V6337"  "V6338"  "V6339"  "V6340"  "V6341"  "V6342"  "V6343"  "V6344" 
##  [6345] "V6345"  "V6346"  "V6347"  "V6348"  "V6349"  "V6350"  "V6351"  "V6352" 
##  [6353] "V6353"  "V6354"  "V6355"  "V6356"  "V6357"  "V6358"  "V6359"  "V6360" 
##  [6361] "V6361"  "V6362"  "V6363"  "V6364"  "V6365"  "V6366"  "V6367"  "V6368" 
##  [6369] "V6369"  "V6370"  "V6371"  "V6372"  "V6373"  "V6374"  "V6375"  "V6376" 
##  [6377] "V6377"  "V6378"  "V6379"  "V6380"  "V6381"  "V6382"  "V6383"  "V6384" 
##  [6385] "V6385"  "V6386"  "V6387"  "V6388"  "V6389"  "V6390"  "V6391"  "V6392" 
##  [6393] "V6393"  "V6394"  "V6395"  "V6396"  "V6397"  "V6398"  "V6399"  "V6400" 
##  [6401] "V6401"  "V6402"  "V6403"  "V6404"  "V6405"  "V6406"  "V6407"  "V6408" 
##  [6409] "V6409"  "V6410"  "V6411"  "V6412"  "V6413"  "V6414"  "V6415"  "V6416" 
##  [6417] "V6417"  "V6418"  "V6419"  "V6420"  "V6421"  "V6422"  "V6423"  "V6424" 
##  [6425] "V6425"  "V6426"  "V6427"  "V6428"  "V6429"  "V6430"  "V6431"  "V6432" 
##  [6433] "V6433"  "V6434"  "V6435"  "V6436"  "V6437"  "V6438"  "V6439"  "V6440" 
##  [6441] "V6441"  "V6442"  "V6443"  "V6444"  "V6445"  "V6446"  "V6447"  "V6448" 
##  [6449] "V6449"  "V6450"  "V6451"  "V6452"  "V6453"  "V6454"  "V6455"  "V6456" 
##  [6457] "V6457"  "V6458"  "V6459"  "V6460"  "V6461"  "V6462"  "V6463"  "V6464" 
##  [6465] "V6465"  "V6466"  "V6467"  "V6468"  "V6469"  "V6470"  "V6471"  "V6472" 
##  [6473] "V6473"  "V6474"  "V6475"  "V6476"  "V6477"  "V6478"  "V6479"  "V6480" 
##  [6481] "V6481"  "V6482"  "V6483"  "V6484"  "V6485"  "V6486"  "V6487"  "V6488" 
##  [6489] "V6489"  "V6490"  "V6491"  "V6492"  "V6493"  "V6494"  "V6495"  "V6496" 
##  [6497] "V6497"  "V6498"  "V6499"  "V6500"  "V6501"  "V6502"  "V6503"  "V6504" 
##  [6505] "V6505"  "V6506"  "V6507"  "V6508"  "V6509"  "V6510"  "V6511"  "V6512" 
##  [6513] "V6513"  "V6514"  "V6515"  "V6516"  "V6517"  "V6518"  "V6519"  "V6520" 
##  [6521] "V6521"  "V6522"  "V6523"  "V6524"  "V6525"  "V6526"  "V6527"  "V6528" 
##  [6529] "V6529"  "V6530"  "V6531"  "V6532"  "V6533"  "V6534"  "V6535"  "V6536" 
##  [6537] "V6537"  "V6538"  "V6539"  "V6540"  "V6541"  "V6542"  "V6543"  "V6544" 
##  [6545] "V6545"  "V6546"  "V6547"  "V6548"  "V6549"  "V6550"  "V6551"  "V6552" 
##  [6553] "V6553"  "V6554"  "V6555"  "V6556"  "V6557"  "V6558"  "V6559"  "V6560" 
##  [6561] "V6561"  "V6562"  "V6563"  "V6564"  "V6565"  "V6566"  "V6567"  "V6568" 
##  [6569] "V6569"  "V6570"  "V6571"  "V6572"  "V6573"  "V6574"  "V6575"  "V6576" 
##  [6577] "V6577"  "V6578"  "V6579"  "V6580"  "V6581"  "V6582"  "V6583"  "V6584" 
##  [6585] "V6585"  "V6586"  "V6587"  "V6588"  "V6589"  "V6590"  "V6591"  "V6592" 
##  [6593] "V6593"  "V6594"  "V6595"  "V6596"  "V6597"  "V6598"  "V6599"  "V6600" 
##  [6601] "V6601"  "V6602"  "V6603"  "V6604"  "V6605"  "V6606"  "V6607"  "V6608" 
##  [6609] "V6609"  "V6610"  "V6611"  "V6612"  "V6613"  "V6614"  "V6615"  "V6616" 
##  [6617] "V6617"  "V6618"  "V6619"  "V6620"  "V6621"  "V6622"  "V6623"  "V6624" 
##  [6625] "V6625"  "V6626"  "V6627"  "V6628"  "V6629"  "V6630"  "V6631"  "V6632" 
##  [6633] "V6633"  "V6634"  "V6635"  "V6636"  "V6637"  "V6638"  "V6639"  "V6640" 
##  [6641] "V6641"  "V6642"  "V6643"  "V6644"  "V6645"  "V6646"  "V6647"  "V6648" 
##  [6649] "V6649"  "V6650"  "V6651"  "V6652"  "V6653"  "V6654"  "V6655"  "V6656" 
##  [6657] "V6657"  "V6658"  "V6659"  "V6660"  "V6661"  "V6662"  "V6663"  "V6664" 
##  [6665] "V6665"  "V6666"  "V6667"  "V6668"  "V6669"  "V6670"  "V6671"  "V6672" 
##  [6673] "V6673"  "V6674"  "V6675"  "V6676"  "V6677"  "V6678"  "V6679"  "V6680" 
##  [6681] "V6681"  "V6682"  "V6683"  "V6684"  "V6685"  "V6686"  "V6687"  "V6688" 
##  [6689] "V6689"  "V6690"  "V6691"  "V6692"  "V6693"  "V6694"  "V6695"  "V6696" 
##  [6697] "V6697"  "V6698"  "V6699"  "V6700"  "V6701"  "V6702"  "V6703"  "V6704" 
##  [6705] "V6705"  "V6706"  "V6707"  "V6708"  "V6709"  "V6710"  "V6711"  "V6712" 
##  [6713] "V6713"  "V6714"  "V6715"  "V6716"  "V6717"  "V6718"  "V6719"  "V6720" 
##  [6721] "V6721"  "V6722"  "V6723"  "V6724"  "V6725"  "V6726"  "V6727"  "V6728" 
##  [6729] "V6729"  "V6730"  "V6731"  "V6732"  "V6733"  "V6734"  "V6735"  "V6736" 
##  [6737] "V6737"  "V6738"  "V6739"  "V6740"  "V6741"  "V6742"  "V6743"  "V6744" 
##  [6745] "V6745"  "V6746"  "V6747"  "V6748"  "V6749"  "V6750"  "V6751"  "V6752" 
##  [6753] "V6753"  "V6754"  "V6755"  "V6756"  "V6757"  "V6758"  "V6759"  "V6760" 
##  [6761] "V6761"  "V6762"  "V6763"  "V6764"  "V6765"  "V6766"  "V6767"  "V6768" 
##  [6769] "V6769"  "V6770"  "V6771"  "V6772"  "V6773"  "V6774"  "V6775"  "V6776" 
##  [6777] "V6777"  "V6778"  "V6779"  "V6780"  "V6781"  "V6782"  "V6783"  "V6784" 
##  [6785] "V6785"  "V6786"  "V6787"  "V6788"  "V6789"  "V6790"  "V6791"  "V6792" 
##  [6793] "V6793"  "V6794"  "V6795"  "V6796"  "V6797"  "V6798"  "V6799"  "V6800" 
##  [6801] "V6801"  "V6802"  "V6803"  "V6804"  "V6805"  "V6806"  "V6807"  "V6808" 
##  [6809] "V6809"  "V6810"  "V6811"  "V6812"  "V6813"  "V6814"  "V6815"  "V6816" 
##  [6817] "V6817"  "V6818"  "V6819"  "V6820"  "V6821"  "V6822"  "V6823"  "V6824" 
##  [6825] "V6825"  "V6826"  "V6827"  "V6828"  "V6829"  "V6830"  "V6831"  "V6832" 
##  [6833] "V6833"  "V6834"  "V6835"  "V6836"  "V6837"  "V6838"  "V6839"  "V6840" 
##  [6841] "V6841"  "V6842"  "V6843"  "V6844"  "V6845"  "V6846"  "V6847"  "V6848" 
##  [6849] "V6849"  "V6850"  "V6851"  "V6852"  "V6853"  "V6854"  "V6855"  "V6856" 
##  [6857] "V6857"  "V6858"  "V6859"  "V6860"  "V6861"  "V6862"  "V6863"  "V6864" 
##  [6865] "V6865"  "V6866"  "V6867"  "V6868"  "V6869"  "V6870"  "V6871"  "V6872" 
##  [6873] "V6873"  "V6874"  "V6875"  "V6876"  "V6877"  "V6878"  "V6879"  "V6880" 
##  [6881] "V6881"  "V6882"  "V6883"  "V6884"  "V6885"  "V6886"  "V6887"  "V6888" 
##  [6889] "V6889"  "V6890"  "V6891"  "V6892"  "V6893"  "V6894"  "V6895"  "V6896" 
##  [6897] "V6897"  "V6898"  "V6899"  "V6900"  "V6901"  "V6902"  "V6903"  "V6904" 
##  [6905] "V6905"  "V6906"  "V6907"  "V6908"  "V6909"  "V6910"  "V6911"  "V6912" 
##  [6913] "V6913"  "V6914"  "V6915"  "V6916"  "V6917"  "V6918"  "V6919"  "V6920" 
##  [6921] "V6921"  "V6922"  "V6923"  "V6924"  "V6925"  "V6926"  "V6927"  "V6928" 
##  [6929] "V6929"  "V6930"  "V6931"  "V6932"  "V6933"  "V6934"  "V6935"  "V6936" 
##  [6937] "V6937"  "V6938"  "V6939"  "V6940"  "V6941"  "V6942"  "V6943"  "V6944" 
##  [6945] "V6945"  "V6946"  "V6947"  "V6948"  "V6949"  "V6950"  "V6951"  "V6952" 
##  [6953] "V6953"  "V6954"  "V6955"  "V6956"  "V6957"  "V6958"  "V6959"  "V6960" 
##  [6961] "V6961"  "V6962"  "V6963"  "V6964"  "V6965"  "V6966"  "V6967"  "V6968" 
##  [6969] "V6969"  "V6970"  "V6971"  "V6972"  "V6973"  "V6974"  "V6975"  "V6976" 
##  [6977] "V6977"  "V6978"  "V6979"  "V6980"  "V6981"  "V6982"  "V6983"  "V6984" 
##  [6985] "V6985"  "V6986"  "V6987"  "V6988"  "V6989"  "V6990"  "V6991"  "V6992" 
##  [6993] "V6993"  "V6994"  "V6995"  "V6996"  "V6997"  "V6998"  "V6999"  "V7000" 
##  [7001] "V7001"  "V7002"  "V7003"  "V7004"  "V7005"  "V7006"  "V7007"  "V7008" 
##  [7009] "V7009"  "V7010"  "V7011"  "V7012"  "V7013"  "V7014"  "V7015"  "V7016" 
##  [7017] "V7017"  "V7018"  "V7019"  "V7020"  "V7021"  "V7022"  "V7023"  "V7024" 
##  [7025] "V7025"  "V7026"  "V7027"  "V7028"  "V7029"  "V7030"  "V7031"  "V7032" 
##  [7033] "V7033"  "V7034"  "V7035"  "V7036"  "V7037"  "V7038"  "V7039"  "V7040" 
##  [7041] "V7041"  "V7042"  "V7043"  "V7044"  "V7045"  "V7046"  "V7047"  "V7048" 
##  [7049] "V7049"  "V7050"  "V7051"  "V7052"  "V7053"  "V7054"  "V7055"  "V7056" 
##  [7057] "V7057"  "V7058"  "V7059"  "V7060"  "V7061"  "V7062"  "V7063"  "V7064" 
##  [7065] "V7065"  "V7066"  "V7067"  "V7068"  "V7069"  "V7070"  "V7071"  "V7072" 
##  [7073] "V7073"  "V7074"  "V7075"  "V7076"  "V7077"  "V7078"  "V7079"  "V7080" 
##  [7081] "V7081"  "V7082"  "V7083"  "V7084"  "V7085"  "V7086"  "V7087"  "V7088" 
##  [7089] "V7089"  "V7090"  "V7091"  "V7092"  "V7093"  "V7094"  "V7095"  "V7096" 
##  [7097] "V7097"  "V7098"  "V7099"  "V7100"  "V7101"  "V7102"  "V7103"  "V7104" 
##  [7105] "V7105"  "V7106"  "V7107"  "V7108"  "V7109"  "V7110"  "V7111"  "V7112" 
##  [7113] "V7113"  "V7114"  "V7115"  "V7116"  "V7117"  "V7118"  "V7119"  "V7120" 
##  [7121] "V7121"  "V7122"  "V7123"  "V7124"  "V7125"  "V7126"  "V7127"  "V7128" 
##  [7129] "V7129"  "V7130"  "V7131"  "V7132"  "V7133"  "V7134"  "V7135"  "V7136" 
##  [7137] "V7137"  "V7138"  "V7139"  "V7140"  "V7141"  "V7142"  "V7143"  "V7144" 
##  [7145] "V7145"  "V7146"  "V7147"  "V7148"  "V7149"  "V7150"  "V7151"  "V7152" 
##  [7153] "V7153"  "V7154"  "V7155"  "V7156"  "V7157"  "V7158"  "V7159"  "V7160" 
##  [7161] "V7161"  "V7162"  "V7163"  "V7164"  "V7165"  "V7166"  "V7167"  "V7168" 
##  [7169] "V7169"  "V7170"  "V7171"  "V7172"  "V7173"  "V7174"  "V7175"  "V7176" 
##  [7177] "V7177"  "V7178"  "V7179"  "V7180"  "V7181"  "V7182"  "V7183"  "V7184" 
##  [7185] "V7185"  "V7186"  "V7187"  "V7188"  "V7189"  "V7190"  "V7191"  "V7192" 
##  [7193] "V7193"  "V7194"  "V7195"  "V7196"  "V7197"  "V7198"  "V7199"  "V7200" 
##  [7201] "V7201"  "V7202"  "V7203"  "V7204"  "V7205"  "V7206"  "V7207"  "V7208" 
##  [7209] "V7209"  "V7210"  "V7211"  "V7212"  "V7213"  "V7214"  "V7215"  "V7216" 
##  [7217] "V7217"  "V7218"  "V7219"  "V7220"  "V7221"  "V7222"  "V7223"  "V7224" 
##  [7225] "V7225"  "V7226"  "V7227"  "V7228"  "V7229"  "V7230"  "V7231"  "V7232" 
##  [7233] "V7233"  "V7234"  "V7235"  "V7236"  "V7237"  "V7238"  "V7239"  "V7240" 
##  [7241] "V7241"  "V7242"  "V7243"  "V7244"  "V7245"  "V7246"  "V7247"  "V7248" 
##  [7249] "V7249"  "V7250"  "V7251"  "V7252"  "V7253"  "V7254"  "V7255"  "V7256" 
##  [7257] "V7257"  "V7258"  "V7259"  "V7260"  "V7261"  "V7262"  "V7263"  "V7264" 
##  [7265] "V7265"  "V7266"  "V7267"  "V7268"  "V7269"  "V7270"  "V7271"  "V7272" 
##  [7273] "V7273"  "V7274"  "V7275"  "V7276"  "V7277"  "V7278"  "V7279"  "V7280" 
##  [7281] "V7281"  "V7282"  "V7283"  "V7284"  "V7285"  "V7286"  "V7287"  "V7288" 
##  [7289] "V7289"  "V7290"  "V7291"  "V7292"  "V7293"  "V7294"  "V7295"  "V7296" 
##  [7297] "V7297"  "V7298"  "V7299"  "V7300"  "V7301"  "V7302"  "V7303"  "V7304" 
##  [7305] "V7305"  "V7306"  "V7307"  "V7308"  "V7309"  "V7310"  "V7311"  "V7312" 
##  [7313] "V7313"  "V7314"  "V7315"  "V7316"  "V7317"  "V7318"  "V7319"  "V7320" 
##  [7321] "V7321"  "V7322"  "V7323"  "V7324"  "V7325"  "V7326"  "V7327"  "V7328" 
##  [7329] "V7329"  "V7330"  "V7331"  "V7332"  "V7333"  "V7334"  "V7335"  "V7336" 
##  [7337] "V7337"  "V7338"  "V7339"  "V7340"  "V7341"  "V7342"  "V7343"  "V7344" 
##  [7345] "V7345"  "V7346"  "V7347"  "V7348"  "V7349"  "V7350"  "V7351"  "V7352" 
##  [7353] "V7353"  "V7354"  "V7355"  "V7356"  "V7357"  "V7358"  "V7359"  "V7360" 
##  [7361] "V7361"  "V7362"  "V7363"  "V7364"  "V7365"  "V7366"  "V7367"  "V7368" 
##  [7369] "V7369"  "V7370"  "V7371"  "V7372"  "V7373"  "V7374"  "V7375"  "V7376" 
##  [7377] "V7377"  "V7378"  "V7379"  "V7380"  "V7381"  "V7382"  "V7383"  "V7384" 
##  [7385] "V7385"  "V7386"  "V7387"  "V7388"  "V7389"  "V7390"  "V7391"  "V7392" 
##  [7393] "V7393"  "V7394"  "V7395"  "V7396"  "V7397"  "V7398"  "V7399"  "V7400" 
##  [7401] "V7401"  "V7402"  "V7403"  "V7404"  "V7405"  "V7406"  "V7407"  "V7408" 
##  [7409] "V7409"  "V7410"  "V7411"  "V7412"  "V7413"  "V7414"  "V7415"  "V7416" 
##  [7417] "V7417"  "V7418"  "V7419"  "V7420"  "V7421"  "V7422"  "V7423"  "V7424" 
##  [7425] "V7425"  "V7426"  "V7427"  "V7428"  "V7429"  "V7430"  "V7431"  "V7432" 
##  [7433] "V7433"  "V7434"  "V7435"  "V7436"  "V7437"  "V7438"  "V7439"  "V7440" 
##  [7441] "V7441"  "V7442"  "V7443"  "V7444"  "V7445"  "V7446"  "V7447"  "V7448" 
##  [7449] "V7449"  "V7450"  "V7451"  "V7452"  "V7453"  "V7454"  "V7455"  "V7456" 
##  [7457] "V7457"  "V7458"  "V7459"  "V7460"  "V7461"  "V7462"  "V7463"  "V7464" 
##  [7465] "V7465"  "V7466"  "V7467"  "V7468"  "V7469"  "V7470"  "V7471"  "V7472" 
##  [7473] "V7473"  "V7474"  "V7475"  "V7476"  "V7477"  "V7478"  "V7479"  "V7480" 
##  [7481] "V7481"  "V7482"  "V7483"  "V7484"  "V7485"  "V7486"  "V7487"  "V7488" 
##  [7489] "V7489"  "V7490"  "V7491"  "V7492"  "V7493"  "V7494"  "V7495"  "V7496" 
##  [7497] "V7497"  "V7498"  "V7499"  "V7500"  "V7501"  "V7502"  "V7503"  "V7504" 
##  [7505] "V7505"  "V7506"  "V7507"  "V7508"  "V7509"  "V7510"  "V7511"  "V7512" 
##  [7513] "V7513"  "V7514"  "V7515"  "V7516"  "V7517"  "V7518"  "V7519"  "V7520" 
##  [7521] "V7521"  "V7522"  "V7523"  "V7524"  "V7525"  "V7526"  "V7527"  "V7528" 
##  [7529] "V7529"  "V7530"  "V7531"  "V7532"  "V7533"  "V7534"  "V7535"  "V7536" 
##  [7537] "V7537"  "V7538"  "V7539"  "V7540"  "V7541"  "V7542"  "V7543"  "V7544" 
##  [7545] "V7545"  "V7546"  "V7547"  "V7548"  "V7549"  "V7550"  "V7551"  "V7552" 
##  [7553] "V7553"  "V7554"  "V7555"  "V7556"  "V7557"  "V7558"  "V7559"  "V7560" 
##  [7561] "V7561"  "V7562"  "V7563"  "V7564"  "V7565"  "V7566"  "V7567"  "V7568" 
##  [7569] "V7569"  "V7570"  "V7571"  "V7572"  "V7573"  "V7574"  "V7575"  "V7576" 
##  [7577] "V7577"  "V7578"  "V7579"  "V7580"  "V7581"  "V7582"  "V7583"  "V7584" 
##  [7585] "V7585"  "V7586"  "V7587"  "V7588"  "V7589"  "V7590"  "V7591"  "V7592" 
##  [7593] "V7593"  "V7594"  "V7595"  "V7596"  "V7597"  "V7598"  "V7599"  "V7600" 
##  [7601] "V7601"  "V7602"  "V7603"  "V7604"  "V7605"  "V7606"  "V7607"  "V7608" 
##  [7609] "V7609"  "V7610"  "V7611"  "V7612"  "V7613"  "V7614"  "V7615"  "V7616" 
##  [7617] "V7617"  "V7618"  "V7619"  "V7620"  "V7621"  "V7622"  "V7623"  "V7624" 
##  [7625] "V7625"  "V7626"  "V7627"  "V7628"  "V7629"  "V7630"  "V7631"  "V7632" 
##  [7633] "V7633"  "V7634"  "V7635"  "V7636"  "V7637"  "V7638"  "V7639"  "V7640" 
##  [7641] "V7641"  "V7642"  "V7643"  "V7644"  "V7645"  "V7646"  "V7647"  "V7648" 
##  [7649] "V7649"  "V7650"  "V7651"  "V7652"  "V7653"  "V7654"  "V7655"  "V7656" 
##  [7657] "V7657"  "V7658"  "V7659"  "V7660"  "V7661"  "V7662"  "V7663"  "V7664" 
##  [7665] "V7665"  "V7666"  "V7667"  "V7668"  "V7669"  "V7670"  "V7671"  "V7672" 
##  [7673] "V7673"  "V7674"  "V7675"  "V7676"  "V7677"  "V7678"  "V7679"  "V7680" 
##  [7681] "V7681"  "V7682"  "V7683"  "V7684"  "V7685"  "V7686"  "V7687"  "V7688" 
##  [7689] "V7689"  "V7690"  "V7691"  "V7692"  "V7693"  "V7694"  "V7695"  "V7696" 
##  [7697] "V7697"  "V7698"  "V7699"  "V7700"  "V7701"  "V7702"  "V7703"  "V7704" 
##  [7705] "V7705"  "V7706"  "V7707"  "V7708"  "V7709"  "V7710"  "V7711"  "V7712" 
##  [7713] "V7713"  "V7714"  "V7715"  "V7716"  "V7717"  "V7718"  "V7719"  "V7720" 
##  [7721] "V7721"  "V7722"  "V7723"  "V7724"  "V7725"  "V7726"  "V7727"  "V7728" 
##  [7729] "V7729"  "V7730"  "V7731"  "V7732"  "V7733"  "V7734"  "V7735"  "V7736" 
##  [7737] "V7737"  "V7738"  "V7739"  "V7740"  "V7741"  "V7742"  "V7743"  "V7744" 
##  [7745] "V7745"  "V7746"  "V7747"  "V7748"  "V7749"  "V7750"  "V7751"  "V7752" 
##  [7753] "V7753"  "V7754"  "V7755"  "V7756"  "V7757"  "V7758"  "V7759"  "V7760" 
##  [7761] "V7761"  "V7762"  "V7763"  "V7764"  "V7765"  "V7766"  "V7767"  "V7768" 
##  [7769] "V7769"  "V7770"  "V7771"  "V7772"  "V7773"  "V7774"  "V7775"  "V7776" 
##  [7777] "V7777"  "V7778"  "V7779"  "V7780"  "V7781"  "V7782"  "V7783"  "V7784" 
##  [7785] "V7785"  "V7786"  "V7787"  "V7788"  "V7789"  "V7790"  "V7791"  "V7792" 
##  [7793] "V7793"  "V7794"  "V7795"  "V7796"  "V7797"  "V7798"  "V7799"  "V7800" 
##  [7801] "V7801"  "V7802"  "V7803"  "V7804"  "V7805"  "V7806"  "V7807"  "V7808" 
##  [7809] "V7809"  "V7810"  "V7811"  "V7812"  "V7813"  "V7814"  "V7815"  "V7816" 
##  [7817] "V7817"  "V7818"  "V7819"  "V7820"  "V7821"  "V7822"  "V7823"  "V7824" 
##  [7825] "V7825"  "V7826"  "V7827"  "V7828"  "V7829"  "V7830"  "V7831"  "V7832" 
##  [7833] "V7833"  "V7834"  "V7835"  "V7836"  "V7837"  "V7838"  "V7839"  "V7840" 
##  [7841] "V7841"  "V7842"  "V7843"  "V7844"  "V7845"  "V7846"  "V7847"  "V7848" 
##  [7849] "V7849"  "V7850"  "V7851"  "V7852"  "V7853"  "V7854"  "V7855"  "V7856" 
##  [7857] "V7857"  "V7858"  "V7859"  "V7860"  "V7861"  "V7862"  "V7863"  "V7864" 
##  [7865] "V7865"  "V7866"  "V7867"  "V7868"  "V7869"  "V7870"  "V7871"  "V7872" 
##  [7873] "V7873"  "V7874"  "V7875"  "V7876"  "V7877"  "V7878"  "V7879"  "V7880" 
##  [7881] "V7881"  "V7882"  "V7883"  "V7884"  "V7885"  "V7886"  "V7887"  "V7888" 
##  [7889] "V7889"  "V7890"  "V7891"  "V7892"  "V7893"  "V7894"  "V7895"  "V7896" 
##  [7897] "V7897"  "V7898"  "V7899"  "V7900"  "V7901"  "V7902"  "V7903"  "V7904" 
##  [7905] "V7905"  "V7906"  "V7907"  "V7908"  "V7909"  "V7910"  "V7911"  "V7912" 
##  [7913] "V7913"  "V7914"  "V7915"  "V7916"  "V7917"  "V7918"  "V7919"  "V7920" 
##  [7921] "V7921"  "V7922"  "V7923"  "V7924"  "V7925"  "V7926"  "V7927"  "V7928" 
##  [7929] "V7929"  "V7930"  "V7931"  "V7932"  "V7933"  "V7934"  "V7935"  "V7936" 
##  [7937] "V7937"  "V7938"  "V7939"  "V7940"  "V7941"  "V7942"  "V7943"  "V7944" 
##  [7945] "V7945"  "V7946"  "V7947"  "V7948"  "V7949"  "V7950"  "V7951"  "V7952" 
##  [7953] "V7953"  "V7954"  "V7955"  "V7956"  "V7957"  "V7958"  "V7959"  "V7960" 
##  [7961] "V7961"  "V7962"  "V7963"  "V7964"  "V7965"  "V7966"  "V7967"  "V7968" 
##  [7969] "V7969"  "V7970"  "V7971"  "V7972"  "V7973"  "V7974"  "V7975"  "V7976" 
##  [7977] "V7977"  "V7978"  "V7979"  "V7980"  "V7981"  "V7982"  "V7983"  "V7984" 
##  [7985] "V7985"  "V7986"  "V7987"  "V7988"  "V7989"  "V7990"  "V7991"  "V7992" 
##  [7993] "V7993"  "V7994"  "V7995"  "V7996"  "V7997"  "V7998"  "V7999"  "V8000" 
##  [8001] "V8001"  "V8002"  "V8003"  "V8004"  "V8005"  "V8006"  "V8007"  "V8008" 
##  [8009] "V8009"  "V8010"  "V8011"  "V8012"  "V8013"  "V8014"  "V8015"  "V8016" 
##  [8017] "V8017"  "V8018"  "V8019"  "V8020"  "V8021"  "V8022"  "V8023"  "V8024" 
##  [8025] "V8025"  "V8026"  "V8027"  "V8028"  "V8029"  "V8030"  "V8031"  "V8032" 
##  [8033] "V8033"  "V8034"  "V8035"  "V8036"  "V8037"  "V8038"  "V8039"  "V8040" 
##  [8041] "V8041"  "V8042"  "V8043"  "V8044"  "V8045"  "V8046"  "V8047"  "V8048" 
##  [8049] "V8049"  "V8050"  "V8051"  "V8052"  "V8053"  "V8054"  "V8055"  "V8056" 
##  [8057] "V8057"  "V8058"  "V8059"  "V8060"  "V8061"  "V8062"  "V8063"  "V8064" 
##  [8065] "V8065"  "V8066"  "V8067"  "V8068"  "V8069"  "V8070"  "V8071"  "V8072" 
##  [8073] "V8073"  "V8074"  "V8075"  "V8076"  "V8077"  "V8078"  "V8079"  "V8080" 
##  [8081] "V8081"  "V8082"  "V8083"  "V8084"  "V8085"  "V8086"  "V8087"  "V8088" 
##  [8089] "V8089"  "V8090"  "V8091"  "V8092"  "V8093"  "V8094"  "V8095"  "V8096" 
##  [8097] "V8097"  "V8098"  "V8099"  "V8100"  "V8101"  "V8102"  "V8103"  "V8104" 
##  [8105] "V8105"  "V8106"  "V8107"  "V8108"  "V8109"  "V8110"  "V8111"  "V8112" 
##  [8113] "V8113"  "V8114"  "V8115"  "V8116"  "V8117"  "V8118"  "V8119"  "V8120" 
##  [8121] "V8121"  "V8122"  "V8123"  "V8124"  "V8125"  "V8126"  "V8127"  "V8128" 
##  [8129] "V8129"  "V8130"  "V8131"  "V8132"  "V8133"  "V8134"  "V8135"  "V8136" 
##  [8137] "V8137"  "V8138"  "V8139"  "V8140"  "V8141"  "V8142"  "V8143"  "V8144" 
##  [8145] "V8145"  "V8146"  "V8147"  "V8148"  "V8149"  "V8150"  "V8151"  "V8152" 
##  [8153] "V8153"  "V8154"  "V8155"  "V8156"  "V8157"  "V8158"  "V8159"  "V8160" 
##  [8161] "V8161"  "V8162"  "V8163"  "V8164"  "V8165"  "V8166"  "V8167"  "V8168" 
##  [8169] "V8169"  "V8170"  "V8171"  "V8172"  "V8173"  "V8174"  "V8175"  "V8176" 
##  [8177] "V8177"  "V8178"  "V8179"  "V8180"  "V8181"  "V8182"  "V8183"  "V8184" 
##  [8185] "V8185"  "V8186"  "V8187"  "V8188"  "V8189"  "V8190"  "V8191"  "V8192" 
##  [8193] "V8193"  "V8194"  "V8195"  "V8196"  "V8197"  "V8198"  "V8199"  "V8200" 
##  [8201] "V8201"  "V8202"  "V8203"  "V8204"  "V8205"  "V8206"  "V8207"  "V8208" 
##  [8209] "V8209"  "V8210"  "V8211"  "V8212"  "V8213"  "V8214"  "V8215"  "V8216" 
##  [8217] "V8217"  "V8218"  "V8219"  "V8220"  "V8221"  "V8222"  "V8223"  "V8224" 
##  [8225] "V8225"  "V8226"  "V8227"  "V8228"  "V8229"  "V8230"  "V8231"  "V8232" 
##  [8233] "V8233"  "V8234"  "V8235"  "V8236"  "V8237"  "V8238"  "V8239"  "V8240" 
##  [8241] "V8241"  "V8242"  "V8243"  "V8244"  "V8245"  "V8246"  "V8247"  "V8248" 
##  [8249] "V8249"  "V8250"  "V8251"  "V8252"  "V8253"  "V8254"  "V8255"  "V8256" 
##  [8257] "V8257"  "V8258"  "V8259"  "V8260"  "V8261"  "V8262"  "V8263"  "V8264" 
##  [8265] "V8265"  "V8266"  "V8267"  "V8268"  "V8269"  "V8270"  "V8271"  "V8272" 
##  [8273] "V8273"  "V8274"  "V8275"  "V8276"  "V8277"  "V8278"  "V8279"  "V8280" 
##  [8281] "V8281"  "V8282"  "V8283"  "V8284"  "V8285"  "V8286"  "V8287"  "V8288" 
##  [8289] "V8289"  "V8290"  "V8291"  "V8292"  "V8293"  "V8294"  "V8295"  "V8296" 
##  [8297] "V8297"  "V8298"  "V8299"  "V8300"  "V8301"  "V8302"  "V8303"  "V8304" 
##  [8305] "V8305"  "V8306"  "V8307"  "V8308"  "V8309"  "V8310"  "V8311"  "V8312" 
##  [8313] "V8313"  "V8314"  "V8315"  "V8316"  "V8317"  "V8318"  "V8319"  "V8320" 
##  [8321] "V8321"  "V8322"  "V8323"  "V8324"  "V8325"  "V8326"  "V8327"  "V8328" 
##  [8329] "V8329"  "V8330"  "V8331"  "V8332"  "V8333"  "V8334"  "V8335"  "V8336" 
##  [8337] "V8337"  "V8338"  "V8339"  "V8340"  "V8341"  "V8342"  "V8343"  "V8344" 
##  [8345] "V8345"  "V8346"  "V8347"  "V8348"  "V8349"  "V8350"  "V8351"  "V8352" 
##  [8353] "V8353"  "V8354"  "V8355"  "V8356"  "V8357"  "V8358"  "V8359"  "V8360" 
##  [8361] "V8361"  "V8362"  "V8363"  "V8364"  "V8365"  "V8366"  "V8367"  "V8368" 
##  [8369] "V8369"  "V8370"  "V8371"  "V8372"  "V8373"  "V8374"  "V8375"  "V8376" 
##  [8377] "V8377"  "V8378"  "V8379"  "V8380"  "V8381"  "V8382"  "V8383"  "V8384" 
##  [8385] "V8385"  "V8386"  "V8387"  "V8388"  "V8389"  "V8390"  "V8391"  "V8392" 
##  [8393] "V8393"  "V8394"  "V8395"  "V8396"  "V8397"  "V8398"  "V8399"  "V8400" 
##  [8401] "V8401"  "V8402"  "V8403"  "V8404"  "V8405"  "V8406"  "V8407"  "V8408" 
##  [8409] "V8409"  "V8410"  "V8411"  "V8412"  "V8413"  "V8414"  "V8415"  "V8416" 
##  [8417] "V8417"  "V8418"  "V8419"  "V8420"  "V8421"  "V8422"  "V8423"  "V8424" 
##  [8425] "V8425"  "V8426"  "V8427"  "V8428"  "V8429"  "V8430"  "V8431"  "V8432" 
##  [8433] "V8433"  "V8434"  "V8435"  "V8436"  "V8437"  "V8438"  "V8439"  "V8440" 
##  [8441] "V8441"  "V8442"  "V8443"  "V8444"  "V8445"  "V8446"  "V8447"  "V8448" 
##  [8449] "V8449"  "V8450"  "V8451"  "V8452"  "V8453"  "V8454"  "V8455"  "V8456" 
##  [8457] "V8457"  "V8458"  "V8459"  "V8460"  "V8461"  "V8462"  "V8463"  "V8464" 
##  [8465] "V8465"  "V8466"  "V8467"  "V8468"  "V8469"  "V8470"  "V8471"  "V8472" 
##  [8473] "V8473"  "V8474"  "V8475"  "V8476"  "V8477"  "V8478"  "V8479"  "V8480" 
##  [8481] "V8481"  "V8482"  "V8483"  "V8484"  "V8485"  "V8486"  "V8487"  "V8488" 
##  [8489] "V8489"  "V8490"  "V8491"  "V8492"  "V8493"  "V8494"  "V8495"  "V8496" 
##  [8497] "V8497"  "V8498"  "V8499"  "V8500"  "V8501"  "V8502"  "V8503"  "V8504" 
##  [8505] "V8505"  "V8506"  "V8507"  "V8508"  "V8509"  "V8510"  "V8511"  "V8512" 
##  [8513] "V8513"  "V8514"  "V8515"  "V8516"  "V8517"  "V8518"  "V8519"  "V8520" 
##  [8521] "V8521"  "V8522"  "V8523"  "V8524"  "V8525"  "V8526"  "V8527"  "V8528" 
##  [8529] "V8529"  "V8530"  "V8531"  "V8532"  "V8533"  "V8534"  "V8535"  "V8536" 
##  [8537] "V8537"  "V8538"  "V8539"  "V8540"  "V8541"  "V8542"  "V8543"  "V8544" 
##  [8545] "V8545"  "V8546"  "V8547"  "V8548"  "V8549"  "V8550"  "V8551"  "V8552" 
##  [8553] "V8553"  "V8554"  "V8555"  "V8556"  "V8557"  "V8558"  "V8559"  "V8560" 
##  [8561] "V8561"  "V8562"  "V8563"  "V8564"  "V8565"  "V8566"  "V8567"  "V8568" 
##  [8569] "V8569"  "V8570"  "V8571"  "V8572"  "V8573"  "V8574"  "V8575"  "V8576" 
##  [8577] "V8577"  "V8578"  "V8579"  "V8580"  "V8581"  "V8582"  "V8583"  "V8584" 
##  [8585] "V8585"  "V8586"  "V8587"  "V8588"  "V8589"  "V8590"  "V8591"  "V8592" 
##  [8593] "V8593"  "V8594"  "V8595"  "V8596"  "V8597"  "V8598"  "V8599"  "V8600" 
##  [8601] "V8601"  "V8602"  "V8603"  "V8604"  "V8605"  "V8606"  "V8607"  "V8608" 
##  [8609] "V8609"  "V8610"  "V8611"  "V8612"  "V8613"  "V8614"  "V8615"  "V8616" 
##  [8617] "V8617"  "V8618"  "V8619"  "V8620"  "V8621"  "V8622"  "V8623"  "V8624" 
##  [8625] "V8625"  "V8626"  "V8627"  "V8628"  "V8629"  "V8630"  "V8631"  "V8632" 
##  [8633] "V8633"  "V8634"  "V8635"  "V8636"  "V8637"  "V8638"  "V8639"  "V8640" 
##  [8641] "V8641"  "V8642"  "V8643"  "V8644"  "V8645"  "V8646"  "V8647"  "V8648" 
##  [8649] "V8649"  "V8650"  "V8651"  "V8652"  "V8653"  "V8654"  "V8655"  "V8656" 
##  [8657] "V8657"  "V8658"  "V8659"  "V8660"  "V8661"  "V8662"  "V8663"  "V8664" 
##  [8665] "V8665"  "V8666"  "V8667"  "V8668"  "V8669"  "V8670"  "V8671"  "V8672" 
##  [8673] "V8673"  "V8674"  "V8675"  "V8676"  "V8677"  "V8678"  "V8679"  "V8680" 
##  [8681] "V8681"  "V8682"  "V8683"  "V8684"  "V8685"  "V8686"  "V8687"  "V8688" 
##  [8689] "V8689"  "V8690"  "V8691"  "V8692"  "V8693"  "V8694"  "V8695"  "V8696" 
##  [8697] "V8697"  "V8698"  "V8699"  "V8700"  "V8701"  "V8702"  "V8703"  "V8704" 
##  [8705] "V8705"  "V8706"  "V8707"  "V8708"  "V8709"  "V8710"  "V8711"  "V8712" 
##  [8713] "V8713"  "V8714"  "V8715"  "V8716"  "V8717"  "V8718"  "V8719"  "V8720" 
##  [8721] "V8721"  "V8722"  "V8723"  "V8724"  "V8725"  "V8726"  "V8727"  "V8728" 
##  [8729] "V8729"  "V8730"  "V8731"  "V8732"  "V8733"  "V8734"  "V8735"  "V8736" 
##  [8737] "V8737"  "V8738"  "V8739"  "V8740"  "V8741"  "V8742"  "V8743"  "V8744" 
##  [8745] "V8745"  "V8746"  "V8747"  "V8748"  "V8749"  "V8750"  "V8751"  "V8752" 
##  [8753] "V8753"  "V8754"  "V8755"  "V8756"  "V8757"  "V8758"  "V8759"  "V8760" 
##  [8761] "V8761"  "V8762"  "V8763"  "V8764"  "V8765"  "V8766"  "V8767"  "V8768" 
##  [8769] "V8769"  "V8770"  "V8771"  "V8772"  "V8773"  "V8774"  "V8775"  "V8776" 
##  [8777] "V8777"  "V8778"  "V8779"  "V8780"  "V8781"  "V8782"  "V8783"  "V8784" 
##  [8785] "V8785"  "V8786"  "V8787"  "V8788"  "V8789"  "V8790"  "V8791"  "V8792" 
##  [8793] "V8793"  "V8794"  "V8795"  "V8796"  "V8797"  "V8798"  "V8799"  "V8800" 
##  [8801] "V8801"  "V8802"  "V8803"  "V8804"  "V8805"  "V8806"  "V8807"  "V8808" 
##  [8809] "V8809"  "V8810"  "V8811"  "V8812"  "V8813"  "V8814"  "V8815"  "V8816" 
##  [8817] "V8817"  "V8818"  "V8819"  "V8820"  "V8821"  "V8822"  "V8823"  "V8824" 
##  [8825] "V8825"  "V8826"  "V8827"  "V8828"  "V8829"  "V8830"  "V8831"  "V8832" 
##  [8833] "V8833"  "V8834"  "V8835"  "V8836"  "V8837"  "V8838"  "V8839"  "V8840" 
##  [8841] "V8841"  "V8842"  "V8843"  "V8844"  "V8845"  "V8846"  "V8847"  "V8848" 
##  [8849] "V8849"  "V8850"  "V8851"  "V8852"  "V8853"  "V8854"  "V8855"  "V8856" 
##  [8857] "V8857"  "V8858"  "V8859"  "V8860"  "V8861"  "V8862"  "V8863"  "V8864" 
##  [8865] "V8865"  "V8866"  "V8867"  "V8868"  "V8869"  "V8870"  "V8871"  "V8872" 
##  [8873] "V8873"  "V8874"  "V8875"  "V8876"  "V8877"  "V8878"  "V8879"  "V8880" 
##  [8881] "V8881"  "V8882"  "V8883"  "V8884"  "V8885"  "V8886"  "V8887"  "V8888" 
##  [8889] "V8889"  "V8890"  "V8891"  "V8892"  "V8893"  "V8894"  "V8895"  "V8896" 
##  [8897] "V8897"  "V8898"  "V8899"  "V8900"  "V8901"  "V8902"  "V8903"  "V8904" 
##  [8905] "V8905"  "V8906"  "V8907"  "V8908"  "V8909"  "V8910"  "V8911"  "V8912" 
##  [8913] "V8913"  "V8914"  "V8915"  "V8916"  "V8917"  "V8918"  "V8919"  "V8920" 
##  [8921] "V8921"  "V8922"  "V8923"  "V8924"  "V8925"  "V8926"  "V8927"  "V8928" 
##  [8929] "V8929"  "V8930"  "V8931"  "V8932"  "V8933"  "V8934"  "V8935"  "V8936" 
##  [8937] "V8937"  "V8938"  "V8939"  "V8940"  "V8941"  "V8942"  "V8943"  "V8944" 
##  [8945] "V8945"  "V8946"  "V8947"  "V8948"  "V8949"  "V8950"  "V8951"  "V8952" 
##  [8953] "V8953"  "V8954"  "V8955"  "V8956"  "V8957"  "V8958"  "V8959"  "V8960" 
##  [8961] "V8961"  "V8962"  "V8963"  "V8964"  "V8965"  "V8966"  "V8967"  "V8968" 
##  [8969] "V8969"  "V8970"  "V8971"  "V8972"  "V8973"  "V8974"  "V8975"  "V8976" 
##  [8977] "V8977"  "V8978"  "V8979"  "V8980"  "V8981"  "V8982"  "V8983"  "V8984" 
##  [8985] "V8985"  "V8986"  "V8987"  "V8988"  "V8989"  "V8990"  "V8991"  "V8992" 
##  [8993] "V8993"  "V8994"  "V8995"  "V8996"  "V8997"  "V8998"  "V8999"  "V9000" 
##  [9001] "V9001"  "V9002"  "V9003"  "V9004"  "V9005"  "V9006"  "V9007"  "V9008" 
##  [9009] "V9009"  "V9010"  "V9011"  "V9012"  "V9013"  "V9014"  "V9015"  "V9016" 
##  [9017] "V9017"  "V9018"  "V9019"  "V9020"  "V9021"  "V9022"  "V9023"  "V9024" 
##  [9025] "V9025"  "V9026"  "V9027"  "V9028"  "V9029"  "V9030"  "V9031"  "V9032" 
##  [9033] "V9033"  "V9034"  "V9035"  "V9036"  "V9037"  "V9038"  "V9039"  "V9040" 
##  [9041] "V9041"  "V9042"  "V9043"  "V9044"  "V9045"  "V9046"  "V9047"  "V9048" 
##  [9049] "V9049"  "V9050"  "V9051"  "V9052"  "V9053"  "V9054"  "V9055"  "V9056" 
##  [9057] "V9057"  "V9058"  "V9059"  "V9060"  "V9061"  "V9062"  "V9063"  "V9064" 
##  [9065] "V9065"  "V9066"  "V9067"  "V9068"  "V9069"  "V9070"  "V9071"  "V9072" 
##  [9073] "V9073"  "V9074"  "V9075"  "V9076"  "V9077"  "V9078"  "V9079"  "V9080" 
##  [9081] "V9081"  "V9082"  "V9083"  "V9084"  "V9085"  "V9086"  "V9087"  "V9088" 
##  [9089] "V9089"  "V9090"  "V9091"  "V9092"  "V9093"  "V9094"  "V9095"  "V9096" 
##  [9097] "V9097"  "V9098"  "V9099"  "V9100"  "V9101"  "V9102"  "V9103"  "V9104" 
##  [9105] "V9105"  "V9106"  "V9107"  "V9108"  "V9109"  "V9110"  "V9111"  "V9112" 
##  [9113] "V9113"  "V9114"  "V9115"  "V9116"  "V9117"  "V9118"  "V9119"  "V9120" 
##  [9121] "V9121"  "V9122"  "V9123"  "V9124"  "V9125"  "V9126"  "V9127"  "V9128" 
##  [9129] "V9129"  "V9130"  "V9131"  "V9132"  "V9133"  "V9134"  "V9135"  "V9136" 
##  [9137] "V9137"  "V9138"  "V9139"  "V9140"  "V9141"  "V9142"  "V9143"  "V9144" 
##  [9145] "V9145"  "V9146"  "V9147"  "V9148"  "V9149"  "V9150"  "V9151"  "V9152" 
##  [9153] "V9153"  "V9154"  "V9155"  "V9156"  "V9157"  "V9158"  "V9159"  "V9160" 
##  [9161] "V9161"  "V9162"  "V9163"  "V9164"  "V9165"  "V9166"  "V9167"  "V9168" 
##  [9169] "V9169"  "V9170"  "V9171"  "V9172"  "V9173"  "V9174"  "V9175"  "V9176" 
##  [9177] "V9177"  "V9178"  "V9179"  "V9180"  "V9181"  "V9182"  "V9183"  "V9184" 
##  [9185] "V9185"  "V9186"  "V9187"  "V9188"  "V9189"  "V9190"  "V9191"  "V9192" 
##  [9193] "V9193"  "V9194"  "V9195"  "V9196"  "V9197"  "V9198"  "V9199"  "V9200" 
##  [9201] "V9201"  "V9202"  "V9203"  "V9204"  "V9205"  "V9206"  "V9207"  "V9208" 
##  [9209] "V9209"  "V9210"  "V9211"  "V9212"  "V9213"  "V9214"  "V9215"  "V9216" 
##  [9217] "V9217"  "V9218"  "V9219"  "V9220"  "V9221"  "V9222"  "V9223"  "V9224" 
##  [9225] "V9225"  "V9226"  "V9227"  "V9228"  "V9229"  "V9230"  "V9231"  "V9232" 
##  [9233] "V9233"  "V9234"  "V9235"  "V9236"  "V9237"  "V9238"  "V9239"  "V9240" 
##  [9241] "V9241"  "V9242"  "V9243"  "V9244"  "V9245"  "V9246"  "V9247"  "V9248" 
##  [9249] "V9249"  "V9250"  "V9251"  "V9252"  "V9253"  "V9254"  "V9255"  "V9256" 
##  [9257] "V9257"  "V9258"  "V9259"  "V9260"  "V9261"  "V9262"  "V9263"  "V9264" 
##  [9265] "V9265"  "V9266"  "V9267"  "V9268"  "V9269"  "V9270"  "V9271"  "V9272" 
##  [9273] "V9273"  "V9274"  "V9275"  "V9276"  "V9277"  "V9278"  "V9279"  "V9280" 
##  [9281] "V9281"  "V9282"  "V9283"  "V9284"  "V9285"  "V9286"  "V9287"  "V9288" 
##  [9289] "V9289"  "V9290"  "V9291"  "V9292"  "V9293"  "V9294"  "V9295"  "V9296" 
##  [9297] "V9297"  "V9298"  "V9299"  "V9300"  "V9301"  "V9302"  "V9303"  "V9304" 
##  [9305] "V9305"  "V9306"  "V9307"  "V9308"  "V9309"  "V9310"  "V9311"  "V9312" 
##  [9313] "V9313"  "V9314"  "V9315"  "V9316"  "V9317"  "V9318"  "V9319"  "V9320" 
##  [9321] "V9321"  "V9322"  "V9323"  "V9324"  "V9325"  "V9326"  "V9327"  "V9328" 
##  [9329] "V9329"  "V9330"  "V9331"  "V9332"  "V9333"  "V9334"  "V9335"  "V9336" 
##  [9337] "V9337"  "V9338"  "V9339"  "V9340"  "V9341"  "V9342"  "V9343"  "V9344" 
##  [9345] "V9345"  "V9346"  "V9347"  "V9348"  "V9349"  "V9350"  "V9351"  "V9352" 
##  [9353] "V9353"  "V9354"  "V9355"  "V9356"  "V9357"  "V9358"  "V9359"  "V9360" 
##  [9361] "V9361"  "V9362"  "V9363"  "V9364"  "V9365"  "V9366"  "V9367"  "V9368" 
##  [9369] "V9369"  "V9370"  "V9371"  "V9372"  "V9373"  "V9374"  "V9375"  "V9376" 
##  [9377] "V9377"  "V9378"  "V9379"  "V9380"  "V9381"  "V9382"  "V9383"  "V9384" 
##  [9385] "V9385"  "V9386"  "V9387"  "V9388"  "V9389"  "V9390"  "V9391"  "V9392" 
##  [9393] "V9393"  "V9394"  "V9395"  "V9396"  "V9397"  "V9398"  "V9399"  "V9400" 
##  [9401] "V9401"  "V9402"  "V9403"  "V9404"  "V9405"  "V9406"  "V9407"  "V9408" 
##  [9409] "V9409"  "V9410"  "V9411"  "V9412"  "V9413"  "V9414"  "V9415"  "V9416" 
##  [9417] "V9417"  "V9418"  "V9419"  "V9420"  "V9421"  "V9422"  "V9423"  "V9424" 
##  [9425] "V9425"  "V9426"  "V9427"  "V9428"  "V9429"  "V9430"  "V9431"  "V9432" 
##  [9433] "V9433"  "V9434"  "V9435"  "V9436"  "V9437"  "V9438"  "V9439"  "V9440" 
##  [9441] "V9441"  "V9442"  "V9443"  "V9444"  "V9445"  "V9446"  "V9447"  "V9448" 
##  [9449] "V9449"  "V9450"  "V9451"  "V9452"  "V9453"  "V9454"  "V9455"  "V9456" 
##  [9457] "V9457"  "V9458"  "V9459"  "V9460"  "V9461"  "V9462"  "V9463"  "V9464" 
##  [9465] "V9465"  "V9466"  "V9467"  "V9468"  "V9469"  "V9470"  "V9471"  "V9472" 
##  [9473] "V9473"  "V9474"  "V9475"  "V9476"  "V9477"  "V9478"  "V9479"  "V9480" 
##  [9481] "V9481"  "V9482"  "V9483"  "V9484"  "V9485"  "V9486"  "V9487"  "V9488" 
##  [9489] "V9489"  "V9490"  "V9491"  "V9492"  "V9493"  "V9494"  "V9495"  "V9496" 
##  [9497] "V9497"  "V9498"  "V9499"  "V9500"  "V9501"  "V9502"  "V9503"  "V9504" 
##  [9505] "V9505"  "V9506"  "V9507"  "V9508"  "V9509"  "V9510"  "V9511"  "V9512" 
##  [9513] "V9513"  "V9514"  "V9515"  "V9516"  "V9517"  "V9518"  "V9519"  "V9520" 
##  [9521] "V9521"  "V9522"  "V9523"  "V9524"  "V9525"  "V9526"  "V9527"  "V9528" 
##  [9529] "V9529"  "V9530"  "V9531"  "V9532"  "V9533"  "V9534"  "V9535"  "V9536" 
##  [9537] "V9537"  "V9538"  "V9539"  "V9540"  "V9541"  "V9542"  "V9543"  "V9544" 
##  [9545] "V9545"  "V9546"  "V9547"  "V9548"  "V9549"  "V9550"  "V9551"  "V9552" 
##  [9553] "V9553"  "V9554"  "V9555"  "V9556"  "V9557"  "V9558"  "V9559"  "V9560" 
##  [9561] "V9561"  "V9562"  "V9563"  "V9564"  "V9565"  "V9566"  "V9567"  "V9568" 
##  [9569] "V9569"  "V9570"  "V9571"  "V9572"  "V9573"  "V9574"  "V9575"  "V9576" 
##  [9577] "V9577"  "V9578"  "V9579"  "V9580"  "V9581"  "V9582"  "V9583"  "V9584" 
##  [9585] "V9585"  "V9586"  "V9587"  "V9588"  "V9589"  "V9590"  "V9591"  "V9592" 
##  [9593] "V9593"  "V9594"  "V9595"  "V9596"  "V9597"  "V9598"  "V9599"  "V9600" 
##  [9601] "V9601"  "V9602"  "V9603"  "V9604"  "V9605"  "V9606"  "V9607"  "V9608" 
##  [9609] "V9609"  "V9610"  "V9611"  "V9612"  "V9613"  "V9614"  "V9615"  "V9616" 
##  [9617] "V9617"  "V9618"  "V9619"  "V9620"  "V9621"  "V9622"  "V9623"  "V9624" 
##  [9625] "V9625"  "V9626"  "V9627"  "V9628"  "V9629"  "V9630"  "V9631"  "V9632" 
##  [9633] "V9633"  "V9634"  "V9635"  "V9636"  "V9637"  "V9638"  "V9639"  "V9640" 
##  [9641] "V9641"  "V9642"  "V9643"  "V9644"  "V9645"  "V9646"  "V9647"  "V9648" 
##  [9649] "V9649"  "V9650"  "V9651"  "V9652"  "V9653"  "V9654"  "V9655"  "V9656" 
##  [9657] "V9657"  "V9658"  "V9659"  "V9660"  "V9661"  "V9662"  "V9663"  "V9664" 
##  [9665] "V9665"  "V9666"  "V9667"  "V9668"  "V9669"  "V9670"  "V9671"  "V9672" 
##  [9673] "V9673"  "V9674"  "V9675"  "V9676"  "V9677"  "V9678"  "V9679"  "V9680" 
##  [9681] "V9681"  "V9682"  "V9683"  "V9684"  "V9685"  "V9686"  "V9687"  "V9688" 
##  [9689] "V9689"  "V9690"  "V9691"  "V9692"  "V9693"  "V9694"  "V9695"  "V9696" 
##  [9697] "V9697"  "V9698"  "V9699"  "V9700"  "V9701"  "V9702"  "V9703"  "V9704" 
##  [9705] "V9705"  "V9706"  "V9707"  "V9708"  "V9709"  "V9710"  "V9711"  "V9712" 
##  [9713] "V9713"  "V9714"  "V9715"  "V9716"  "V9717"  "V9718"  "V9719"  "V9720" 
##  [9721] "V9721"  "V9722"  "V9723"  "V9724"  "V9725"  "V9726"  "V9727"  "V9728" 
##  [9729] "V9729"  "V9730"  "V9731"  "V9732"  "V9733"  "V9734"  "V9735"  "V9736" 
##  [9737] "V9737"  "V9738"  "V9739"  "V9740"  "V9741"  "V9742"  "V9743"  "V9744" 
##  [9745] "V9745"  "V9746"  "V9747"  "V9748"  "V9749"  "V9750"  "V9751"  "V9752" 
##  [9753] "V9753"  "V9754"  "V9755"  "V9756"  "V9757"  "V9758"  "V9759"  "V9760" 
##  [9761] "V9761"  "V9762"  "V9763"  "V9764"  "V9765"  "V9766"  "V9767"  "V9768" 
##  [9769] "V9769"  "V9770"  "V9771"  "V9772"  "V9773"  "V9774"  "V9775"  "V9776" 
##  [9777] "V9777"  "V9778"  "V9779"  "V9780"  "V9781"  "V9782"  "V9783"  "V9784" 
##  [9785] "V9785"  "V9786"  "V9787"  "V9788"  "V9789"  "V9790"  "V9791"  "V9792" 
##  [9793] "V9793"  "V9794"  "V9795"  "V9796"  "V9797"  "V9798"  "V9799"  "V9800" 
##  [9801] "V9801"  "V9802"  "V9803"  "V9804"  "V9805"  "V9806"  "V9807"  "V9808" 
##  [9809] "V9809"  "V9810"  "V9811"  "V9812"  "V9813"  "V9814"  "V9815"  "V9816" 
##  [9817] "V9817"  "V9818"  "V9819"  "V9820"  "V9821"  "V9822"  "V9823"  "V9824" 
##  [9825] "V9825"  "V9826"  "V9827"  "V9828"  "V9829"  "V9830"  "V9831"  "V9832" 
##  [9833] "V9833"  "V9834"  "V9835"  "V9836"  "V9837"  "V9838"  "V9839"  "V9840" 
##  [9841] "V9841"  "V9842"  "V9843"  "V9844"  "V9845"  "V9846"  "V9847"  "V9848" 
##  [9849] "V9849"  "V9850"  "V9851"  "V9852"  "V9853"  "V9854"  "V9855"  "V9856" 
##  [9857] "V9857"  "V9858"  "V9859"  "V9860"  "V9861"  "V9862"  "V9863"  "V9864" 
##  [9865] "V9865"  "V9866"  "V9867"  "V9868"  "V9869"  "V9870"  "V9871"  "V9872" 
##  [9873] "V9873"  "V9874"  "V9875"  "V9876"  "V9877"  "V9878"  "V9879"  "V9880" 
##  [9881] "V9881"  "V9882"  "V9883"  "V9884"  "V9885"  "V9886"  "V9887"  "V9888" 
##  [9889] "V9889"  "V9890"  "V9891"  "V9892"  "V9893"  "V9894"  "V9895"  "V9896" 
##  [9897] "V9897"  "V9898"  "V9899"  "V9900"  "V9901"  "V9902"  "V9903"  "V9904" 
##  [9905] "V9905"  "V9906"  "V9907"  "V9908"  "V9909"  "V9910"  "V9911"  "V9912" 
##  [9913] "V9913"  "V9914"  "V9915"  "V9916"  "V9917"  "V9918"  "V9919"  "V9920" 
##  [9921] "V9921"  "V9922"  "V9923"  "V9924"  "V9925"  "V9926"  "V9927"  "V9928" 
##  [9929] "V9929"  "V9930"  "V9931"  "V9932"  "V9933"  "V9934"  "V9935"  "V9936" 
##  [9937] "V9937"  "V9938"  "V9939"  "V9940"  "V9941"  "V9942"  "V9943"  "V9944" 
##  [9945] "V9945"  "V9946"  "V9947"  "V9948"  "V9949"  "V9950"  "V9951"  "V9952" 
##  [9953] "V9953"  "V9954"  "V9955"  "V9956"  "V9957"  "V9958"  "V9959"  "V9960" 
##  [9961] "V9961"  "V9962"  "V9963"  "V9964"  "V9965"  "V9966"  "V9967"  "V9968" 
##  [9969] "V9969"  "V9970"  "V9971"  "V9972"  "V9973"  "V9974"  "V9975"  "V9976" 
##  [9977] "V9977"  "V9978"  "V9979"  "V9980"  "V9981"  "V9982"  "V9983"  "V9984" 
##  [9985] "V9985"  "V9986"  "V9987"  "V9988"  "V9989"  "V9990"  "V9991"  "V9992" 
##  [9993] "V9993"  "V9994"  "V9995"  "V9996"  "V9997"  "V9998"  "V9999"  "V10000"
## [10001] "V10001" "V10002" "V10003" "V10004" "V10005" "V10006" "V10007" "V10008"
## [10009] "V10009" "V10010" "V10011" "V10012" "V10013" "V10014" "V10015" "V10016"
## [10017] "V10017" "V10018" "V10019" "V10020" "V10021" "V10022" "V10023" "V10024"
## [10025] "V10025" "V10026" "V10027" "V10028" "V10029" "V10030" "V10031" "V10032"
## [10033] "V10033" "V10034" "V10035" "V10036" "V10037" "V10038" "V10039" "V10040"
## [10041] "V10041" "V10042" "V10043" "V10044" "V10045" "V10046" "V10047" "V10048"
## [10049] "V10049" "V10050" "V10051" "V10052" "V10053" "V10054" "V10055" "V10056"
## [10057] "V10057" "V10058" "V10059" "V10060" "V10061" "V10062" "V10063" "V10064"
## [10065] "V10065" "V10066" "V10067" "V10068" "V10069" "V10070" "V10071" "V10072"
## [10073] "V10073" "V10074" "V10075" "V10076" "V10077" "V10078" "V10079" "V10080"
## [10081] "V10081" "V10082" "V10083" "V10084" "V10085" "V10086" "V10087" "V10088"
## [10089] "V10089" "V10090" "V10091" "V10092" "V10093" "V10094" "V10095" "V10096"
## [10097] "V10097" "V10098" "V10099" "V10100" "V10101" "V10102" "V10103" "V10104"
## [10105] "V10105" "V10106" "V10107" "V10108" "V10109" "V10110" "V10111" "V10112"
## [10113] "V10113" "V10114" "V10115" "V10116" "V10117" "V10118" "V10119" "V10120"
## [10121] "V10121" "V10122" "V10123" "V10124" "V10125" "V10126" "V10127" "V10128"
## [10129] "V10129" "V10130" "V10131" "V10132" "V10133" "V10134" "V10135" "V10136"
## [10137] "V10137" "V10138" "V10139" "V10140" "V10141" "V10142" "V10143" "V10144"
## [10145] "V10145" "V10146" "V10147" "V10148" "V10149" "V10150" "V10151" "V10152"
## [10153] "V10153" "V10154" "V10155" "V10156" "V10157" "V10158" "V10159" "V10160"
## [10161] "V10161" "V10162" "V10163" "V10164" "V10165" "V10166" "V10167" "V10168"
## [10169] "V10169" "V10170" "V10171" "V10172" "V10173" "V10174" "V10175" "V10176"
## [10177] "V10177" "V10178" "V10179" "V10180" "V10181" "V10182" "V10183" "V10184"
## [10185] "V10185" "V10186" "V10187" "V10188" "V10189" "V10190" "V10191" "V10192"
## [10193] "V10193" "V10194" "V10195" "V10196" "V10197" "V10198" "V10199" "V10200"
## [10201] "V10201" "V10202" "V10203" "V10204" "V10205" "V10206" "V10207" "V10208"
## [10209] "V10209" "V10210" "V10211" "V10212" "V10213" "V10214" "V10215" "V10216"
## [10217] "V10217" "V10218" "V10219" "V10220" "V10221" "V10222" "V10223" "V10224"
## [10225] "V10225" "V10226" "V10227" "V10228" "V10229" "V10230" "V10231" "V10232"
## [10233] "V10233" "V10234" "V10235" "V10236" "V10237" "V10238" "V10239" "V10240"
## [10241] "V10241" "V10242" "V10243" "V10244" "V10245" "V10246" "V10247" "V10248"
## [10249] "V10249" "V10250" "V10251" "V10252" "V10253" "V10254" "V10255" "V10256"
## [10257] "V10257" "V10258" "V10259" "V10260" "V10261" "V10262" "V10263" "V10264"
## [10265] "V10265" "V10266" "V10267" "V10268" "V10269" "V10270" "V10271" "V10272"
## [10273] "V10273" "V10274" "V10275" "V10276" "V10277" "V10278" "V10279" "V10280"
## [10281] "V10281" "V10282" "V10283" "V10284" "V10285" "V10286" "V10287" "V10288"
## [10289] "V10289" "V10290" "V10291" "V10292" "V10293" "V10294" "V10295" "V10296"
## [10297] "V10297" "V10298" "V10299" "V10300" "V10301" "V10302" "V10303" "V10304"
## [10305] "V10305" "V10306" "V10307" "V10308" "V10309" "V10310" "V10311" "V10312"
## [10313] "V10313" "V10314" "V10315" "V10316" "V10317" "V10318" "V10319" "V10320"
## [10321] "V10321" "V10322" "V10323" "V10324" "V10325" "V10326" "V10327" "V10328"
## [10329] "V10329" "V10330" "V10331" "V10332" "V10333" "V10334" "V10335" "V10336"
## [10337] "V10337" "V10338" "V10339" "V10340" "V10341" "V10342" "V10343" "V10344"
## [10345] "V10345" "V10346" "V10347" "V10348" "V10349" "V10350" "V10351" "V10352"
## [10353] "V10353" "V10354" "V10355" "V10356" "V10357" "V10358" "V10359" "V10360"
## [10361] "V10361" "V10362" "V10363" "V10364" "V10365" "V10366" "V10367" "V10368"
## [10369] "V10369" "V10370" "V10371" "V10372" "V10373" "V10374" "V10375" "V10376"
## [10377] "V10377" "V10378" "V10379" "V10380" "V10381" "V10382" "V10383" "V10384"
## [10385] "V10385" "V10386" "V10387" "V10388" "V10389" "V10390" "V10391" "V10392"
## [10393] "V10393" "V10394" "V10395" "V10396" "V10397" "V10398" "V10399" "V10400"
## [10401] "V10401" "V10402" "V10403" "V10404" "V10405" "V10406" "V10407" "V10408"
## [10409] "V10409" "V10410" "V10411" "V10412" "V10413" "V10414" "V10415" "V10416"
## [10417] "V10417" "V10418" "V10419" "V10420" "V10421" "V10422" "V10423" "V10424"
## [10425] "V10425" "V10426" "V10427" "V10428" "V10429" "V10430" "V10431" "V10432"
## [10433] "V10433" "V10434" "V10435" "V10436" "V10437" "V10438" "V10439" "V10440"
## [10441] "V10441" "V10442" "V10443" "V10444" "V10445" "V10446" "V10447" "V10448"
## [10449] "V10449" "V10450" "V10451" "V10452" "V10453" "V10454" "V10455" "V10456"
## [10457] "V10457" "V10458" "V10459" "V10460" "V10461" "V10462" "V10463" "V10464"
## [10465] "V10465" "V10466" "V10467" "V10468" "V10469" "V10470" "V10471" "V10472"
## [10473] "V10473" "V10474" "V10475" "V10476" "V10477" "V10478" "V10479" "V10480"
## [10481] "V10481" "V10482" "V10483" "V10484" "V10485" "V10486" "V10487" "V10488"
## [10489] "V10489" "V10490" "V10491" "V10492" "V10493" "V10494" "V10495" "V10496"
## [10497] "V10497" "V10498" "V10499" "V10500" "V10501" "V10502" "V10503" "V10504"
## [10505] "V10505" "V10506" "V10507" "V10508" "V10509" "V10510" "V10511" "V10512"
## [10513] "V10513" "V10514" "V10515" "V10516" "V10517" "V10518" "V10519" "V10520"
## [10521] "V10521" "V10522" "V10523" "V10524" "V10525" "V10526" "V10527" "V10528"
## [10529] "V10529" "V10530" "V10531" "V10532" "V10533" "V10534" "V10535" "V10536"
## [10537] "V10537" "V10538" "V10539" "V10540" "V10541" "V10542" "V10543" "V10544"
## [10545] "V10545" "V10546" "V10547" "V10548" "V10549" "V10550" "V10551" "V10552"
## [10553] "V10553" "V10554" "V10555" "V10556" "V10557" "V10558" "V10559" "V10560"
## [10561] "V10561" "V10562" "V10563" "V10564" "V10565" "V10566" "V10567" "V10568"
## [10569] "V10569" "V10570" "V10571" "V10572" "V10573" "V10574" "V10575" "V10576"
## [10577] "V10577" "V10578" "V10579" "V10580" "V10581" "V10582" "V10583" "V10584"
## [10585] "V10585" "V10586" "V10587" "V10588" "V10589" "V10590" "V10591" "V10592"
## [10593] "V10593" "V10594" "V10595" "V10596" "V10597" "V10598" "V10599" "V10600"
## [10601] "V10601" "V10602" "V10603" "V10604" "V10605" "V10606" "V10607" "V10608"
## [10609] "V10609" "V10610" "V10611" "V10612" "V10613" "V10614" "V10615" "V10616"
## [10617] "V10617" "V10618" "V10619" "V10620" "V10621" "V10622" "V10623" "V10624"
## [10625] "V10625" "V10626" "V10627" "V10628" "V10629" "V10630" "V10631" "V10632"
## [10633] "V10633" "V10634" "V10635" "V10636" "V10637" "V10638" "V10639" "V10640"
## [10641] "V10641" "V10642" "V10643" "V10644" "V10645" "V10646" "V10647" "V10648"
## [10649] "V10649" "V10650" "V10651" "V10652" "V10653" "V10654" "V10655" "V10656"
## [10657] "V10657" "V10658" "V10659" "V10660" "V10661" "V10662" "V10663" "V10664"
## [10665] "V10665" "V10666" "V10667" "V10668" "V10669" "V10670" "V10671" "V10672"
## [10673] "V10673" "V10674" "V10675" "V10676" "V10677" "V10678" "V10679" "V10680"
## [10681] "V10681" "V10682" "V10683" "V10684" "V10685" "V10686" "V10687" "V10688"
## [10689] "V10689" "V10690" "V10691" "V10692" "V10693" "V10694" "V10695" "V10696"
## [10697] "V10697" "V10698" "V10699" "V10700" "V10701" "V10702" "V10703" "V10704"
## [10705] "V10705" "V10706" "V10707" "V10708" "V10709" "V10710" "V10711" "V10712"
## [10713] "V10713" "V10714" "V10715" "V10716" "V10717" "V10718" "V10719" "V10720"
## [10721] "V10721" "V10722" "V10723" "V10724" "V10725" "V10726" "V10727" "V10728"
## [10729] "V10729" "V10730" "V10731" "V10732" "V10733" "V10734" "V10735" "V10736"
## [10737] "V10737" "V10738" "V10739" "V10740" "V10741" "V10742" "V10743" "V10744"
## [10745] "V10745" "V10746" "V10747" "V10748" "V10749" "V10750" "V10751" "V10752"
## [10753] "V10753" "V10754" "V10755" "V10756" "V10757" "V10758" "V10759" "V10760"
## [10761] "V10761" "V10762" "V10763" "V10764" "V10765" "V10766" "V10767" "V10768"
## [10769] "V10769" "V10770" "V10771" "V10772" "V10773" "V10774" "V10775" "V10776"
## [10777] "V10777" "V10778" "V10779" "V10780" "V10781" "V10782" "V10783" "V10784"
## [10785] "V10785" "V10786" "V10787" "V10788" "V10789" "V10790" "V10791" "V10792"
## [10793] "V10793" "V10794" "V10795" "V10796" "V10797" "V10798" "V10799" "V10800"
## [10801] "V10801" "V10802" "V10803" "V10804" "V10805" "V10806" "V10807" "V10808"
## [10809] "V10809" "V10810" "V10811" "V10812" "V10813" "V10814" "V10815" "V10816"
## [10817] "V10817" "V10818" "V10819" "V10820" "V10821" "V10822" "V10823" "V10824"
## [10825] "V10825" "V10826" "V10827" "V10828" "V10829" "V10830" "V10831" "V10832"
## [10833] "V10833" "V10834" "V10835" "V10836" "V10837" "V10838" "V10839" "V10840"
## [10841] "V10841" "V10842" "V10843" "V10844" "V10845" "V10846" "V10847" "V10848"
## [10849] "V10849" "V10850" "V10851" "V10852" "V10853" "V10854" "V10855" "V10856"
## [10857] "V10857" "V10858" "V10859" "V10860" "V10861" "V10862" "V10863" "V10864"
## [10865] "V10865" "V10866" "V10867" "V10868" "V10869" "V10870" "V10871" "V10872"
## [10873] "V10873" "V10874" "V10875" "V10876" "V10877" "V10878" "V10879" "V10880"
## [10881] "V10881" "V10882" "V10883" "V10884" "V10885" "V10886" "V10887" "V10888"
## [10889] "V10889" "V10890" "V10891" "V10892" "V10893" "V10894" "V10895" "V10896"
## [10897] "V10897" "V10898" "V10899" "V10900" "V10901" "V10902" "V10903" "V10904"
## [10905] "V10905" "V10906" "V10907" "V10908" "V10909" "V10910" "V10911" "V10912"
## [10913] "V10913" "V10914" "V10915" "V10916" "V10917" "V10918" "V10919" "V10920"
## [10921] "V10921" "V10922" "V10923" "V10924" "V10925" "V10926" "V10927" "V10928"
## [10929] "V10929" "V10930" "V10931" "V10932" "V10933" "V10934" "V10935" "V10936"
## [10937] "V10937" "V10938" "V10939" "V10940" "V10941" "V10942" "V10943" "V10944"
## [10945] "V10945" "V10946" "V10947" "V10948" "V10949" "V10950" "V10951" "V10952"
## [10953] "V10953" "V10954" "V10955" "V10956" "V10957" "V10958" "V10959" "V10960"
## [10961] "V10961" "V10962" "V10963" "V10964" "V10965" "V10966" "V10967" "V10968"
## [10969] "V10969" "V10970" "V10971" "V10972" "V10973" "V10974" "V10975" "V10976"
## [10977] "V10977" "V10978" "V10979" "V10980" "V10981" "V10982" "V10983" "V10984"
## [10985] "V10985" "V10986" "V10987" "V10988" "V10989" "V10990" "V10991" "V10992"
## [10993] "V10993" "V10994" "V10995" "V10996" "V10997" "V10998" "V10999" "V11000"
## [11001] "V11001" "V11002" "V11003" "V11004" "V11005" "V11006" "V11007" "V11008"
## [11009] "V11009" "V11010" "V11011" "V11012" "V11013" "V11014" "V11015" "V11016"
## [11017] "V11017" "V11018" "V11019" "V11020" "V11021" "V11022" "V11023" "V11024"
## [11025] "V11025" "V11026" "V11027" "V11028" "V11029" "V11030" "V11031" "V11032"
## [11033] "V11033" "V11034" "V11035" "V11036" "V11037" "V11038" "V11039" "V11040"
## [11041] "V11041" "V11042" "V11043" "V11044" "V11045" "V11046" "V11047" "V11048"
## [11049] "V11049" "V11050" "V11051" "V11052" "V11053" "V11054" "V11055" "V11056"
## [11057] "V11057" "V11058" "V11059" "V11060" "V11061" "V11062" "V11063" "V11064"
## [11065] "V11065" "V11066" "V11067" "V11068" "V11069" "V11070" "V11071" "V11072"
## [11073] "V11073" "V11074" "V11075" "V11076" "V11077" "V11078" "V11079" "V11080"
## [11081] "V11081" "V11082" "V11083" "V11084" "V11085" "V11086" "V11087" "V11088"
## [11089] "V11089" "V11090" "V11091" "V11092" "V11093" "V11094" "V11095" "V11096"
## [11097] "V11097" "V11098" "V11099" "V11100" "V11101" "V11102" "V11103" "V11104"
## [11105] "V11105" "V11106" "V11107" "V11108" "V11109" "V11110" "V11111" "V11112"
## [11113] "V11113" "V11114" "V11115" "V11116" "V11117" "V11118" "V11119" "V11120"
## [11121] "V11121" "V11122" "V11123" "V11124" "V11125" "V11126" "V11127" "V11128"
## [11129] "V11129" "V11130" "V11131" "V11132" "V11133" "V11134" "V11135" "V11136"
## [11137] "V11137" "V11138" "V11139" "V11140" "V11141" "V11142" "V11143" "V11144"
## [11145] "V11145" "V11146" "V11147" "V11148" "V11149" "V11150" "V11151" "V11152"
## [11153] "V11153" "V11154" "V11155" "V11156" "V11157" "V11158" "V11159" "V11160"
## [11161] "V11161" "V11162" "V11163" "V11164" "V11165" "V11166" "V11167" "V11168"
## [11169] "V11169" "V11170" "V11171" "V11172" "V11173" "V11174" "V11175" "V11176"
## [11177] "V11177" "V11178" "V11179" "V11180" "V11181" "V11182" "V11183" "V11184"
## [11185] "V11185" "V11186" "V11187" "V11188" "V11189" "V11190" "V11191" "V11192"
## [11193] "V11193" "V11194" "V11195" "V11196" "V11197" "V11198" "V11199" "V11200"
## [11201] "V11201" "V11202" "V11203" "V11204" "V11205" "V11206" "V11207" "V11208"
## [11209] "V11209" "V11210" "V11211" "V11212" "V11213" "V11214" "V11215" "V11216"
## [11217] "V11217" "V11218" "V11219" "V11220" "V11221" "V11222" "V11223" "V11224"
## [11225] "V11225" "V11226" "V11227" "V11228" "V11229" "V11230" "V11231" "V11232"
## [11233] "V11233" "V11234" "V11235" "V11236" "V11237" "V11238" "V11239" "V11240"
## [11241] "V11241" "V11242" "V11243" "V11244" "V11245" "V11246" "V11247" "V11248"
## [11249] "V11249" "V11250" "V11251" "V11252" "V11253" "V11254" "V11255" "V11256"
## [11257] "V11257" "V11258" "V11259" "V11260" "V11261" "V11262" "V11263" "V11264"
## [11265] "V11265" "V11266" "V11267" "V11268" "V11269" "V11270" "V11271" "V11272"
## [11273] "V11273" "V11274" "V11275" "V11276" "V11277" "V11278" "V11279" "V11280"
## [11281] "V11281" "V11282" "V11283" "V11284" "V11285" "V11286" "V11287" "V11288"
## [11289] "V11289" "V11290" "V11291" "V11292" "V11293" "V11294" "V11295" "V11296"
## [11297] "V11297" "V11298" "V11299" "V11300" "V11301" "V11302" "V11303" "V11304"
## [11305] "V11305" "V11306" "V11307" "V11308" "V11309" "V11310" "V11311" "V11312"
## [11313] "V11313" "V11314" "V11315" "V11316" "V11317" "V11318" "V11319" "V11320"
## [11321] "V11321" "V11322" "V11323" "V11324" "V11325" "V11326" "V11327" "V11328"
## [11329] "V11329" "V11330" "V11331" "V11332" "V11333" "V11334" "V11335" "V11336"
## [11337] "V11337" "V11338" "V11339" "V11340" "V11341" "V11342" "V11343" "V11344"
## [11345] "V11345" "V11346" "V11347" "V11348" "V11349" "V11350" "V11351" "V11352"
## [11353] "V11353" "V11354" "V11355" "V11356" "V11357" "V11358" "V11359" "V11360"
## [11361] "V11361" "V11362" "V11363" "V11364" "V11365" "V11366" "V11367" "V11368"
## [11369] "V11369" "V11370" "V11371" "V11372" "V11373" "V11374" "V11375" "V11376"
## [11377] "V11377" "V11378" "V11379" "V11380" "V11381" "V11382" "V11383" "V11384"
## [11385] "V11385" "V11386" "V11387" "V11388" "V11389" "V11390" "V11391" "V11392"
## [11393] "V11393" "V11394" "V11395" "V11396" "V11397" "V11398" "V11399" "V11400"
## [11401] "V11401" "V11402" "V11403" "V11404" "V11405" "V11406" "V11407" "V11408"
## [11409] "V11409" "V11410" "V11411" "V11412" "V11413" "V11414" "V11415" "V11416"
## [11417] "V11417" "V11418" "V11419" "V11420" "V11421" "V11422" "V11423" "V11424"
## [11425] "V11425" "V11426" "V11427" "V11428" "V11429" "V11430" "V11431" "V11432"
## [11433] "V11433" "V11434" "V11435" "V11436" "V11437" "V11438" "V11439" "V11440"
## [11441] "V11441" "V11442" "V11443" "V11444" "V11445" "V11446" "V11447" "V11448"
## [11449] "V11449" "V11450" "V11451" "V11452" "V11453" "V11454" "V11455" "V11456"
## [11457] "V11457" "V11458" "V11459" "V11460" "V11461" "V11462" "V11463" "V11464"
## [11465] "V11465" "V11466" "V11467" "V11468" "V11469" "V11470" "V11471" "V11472"
## [11473] "V11473" "V11474" "V11475" "V11476" "V11477" "V11478" "V11479" "V11480"
## [11481] "V11481" "V11482" "V11483" "V11484" "V11485" "V11486" "V11487" "V11488"
## [11489] "V11489" "V11490" "V11491" "V11492" "V11493" "V11494" "V11495" "V11496"
## [11497] "V11497" "V11498" "V11499" "V11500" "V11501" "V11502" "V11503" "V11504"
## [11505] "V11505" "V11506" "V11507" "V11508" "V11509" "V11510" "V11511" "V11512"
## [11513] "V11513" "V11514" "V11515" "V11516" "V11517" "V11518" "V11519" "V11520"
## [11521] "V11521" "V11522" "V11523" "V11524" "V11525" "V11526" "V11527" "V11528"
## [11529] "V11529" "V11530" "V11531" "V11532" "V11533" "V11534" "V11535" "V11536"
## [11537] "V11537" "V11538" "V11539" "V11540" "V11541" "V11542" "V11543" "V11544"
## [11545] "V11545" "V11546" "V11547" "V11548" "V11549" "V11550" "V11551" "V11552"
## [11553] "V11553" "V11554" "V11555" "V11556" "V11557" "V11558" "V11559" "V11560"
## [11561] "V11561" "V11562" "V11563" "V11564" "V11565" "V11566" "V11567" "V11568"
## [11569] "V11569" "V11570" "V11571" "V11572" "V11573" "V11574" "V11575" "V11576"
## [11577] "V11577" "V11578" "V11579" "V11580" "V11581" "V11582" "V11583" "V11584"
## [11585] "V11585" "V11586" "V11587" "V11588" "V11589" "V11590" "V11591" "V11592"
## [11593] "V11593" "V11594" "V11595" "V11596" "V11597" "V11598" "V11599" "V11600"
## [11601] "V11601" "V11602" "V11603" "V11604" "V11605" "V11606" "V11607" "V11608"
## [11609] "V11609" "V11610" "V11611" "V11612" "V11613" "V11614" "V11615" "V11616"
## [11617] "V11617" "V11618" "V11619" "V11620" "V11621" "V11622" "V11623" "V11624"
## [11625] "V11625" "V11626" "V11627" "V11628" "V11629" "V11630" "V11631" "V11632"
## [11633] "V11633" "V11634" "V11635" "V11636" "V11637" "V11638" "V11639" "V11640"
## [11641] "V11641" "V11642" "V11643" "V11644" "V11645" "V11646" "V11647" "V11648"
## [11649] "V11649" "V11650" "V11651" "V11652" "V11653" "V11654" "V11655" "V11656"
## [11657] "V11657" "V11658" "V11659" "V11660" "V11661" "V11662" "V11663" "V11664"
## [11665] "V11665" "V11666" "V11667" "V11668" "V11669" "V11670" "V11671" "V11672"
## [11673] "V11673" "V11674" "V11675" "V11676" "V11677" "V11678" "V11679" "V11680"
## [11681] "V11681" "V11682" "V11683" "V11684" "V11685" "V11686" "V11687" "V11688"
## [11689] "V11689" "V11690" "V11691" "V11692" "V11693" "V11694" "V11695" "V11696"
## [11697] "V11697" "V11698" "V11699" "V11700" "V11701" "V11702" "V11703" "V11704"
## [11705] "V11705" "V11706" "V11707" "V11708" "V11709" "V11710" "V11711" "V11712"
## [11713] "V11713" "V11714" "V11715" "V11716" "V11717" "V11718" "V11719" "V11720"
## [11721] "V11721" "V11722" "V11723" "V11724" "V11725" "V11726" "V11727" "V11728"
## [11729] "V11729" "V11730" "V11731" "V11732" "V11733" "V11734" "V11735" "V11736"
## [11737] "V11737" "V11738" "V11739" "V11740" "V11741" "V11742" "V11743" "V11744"
## [11745] "V11745" "V11746" "V11747" "V11748" "V11749" "V11750" "V11751" "V11752"
## [11753] "V11753" "V11754" "V11755" "V11756" "V11757" "V11758" "V11759" "V11760"
## [11761] "V11761" "V11762" "V11763" "V11764" "V11765" "V11766" "V11767" "V11768"
## [11769] "V11769" "V11770" "V11771" "V11772" "V11773" "V11774" "V11775" "V11776"
## [11777] "V11777" "V11778" "V11779" "V11780" "V11781" "V11782" "V11783" "V11784"
## [11785] "V11785" "V11786" "V11787" "V11788" "V11789" "V11790" "V11791" "V11792"
## [11793] "V11793" "V11794" "V11795" "V11796" "V11797" "V11798" "V11799" "V11800"
## [11801] "V11801" "V11802" "V11803" "V11804" "V11805" "V11806" "V11807" "V11808"
## [11809] "V11809" "V11810" "V11811" "V11812" "V11813" "V11814" "V11815" "V11816"
## [11817] "V11817" "V11818" "V11819" "V11820" "V11821" "V11822" "V11823" "V11824"
## [11825] "V11825" "V11826" "V11827" "V11828" "V11829" "V11830" "V11831" "V11832"
## [11833] "V11833" "V11834" "V11835" "V11836" "V11837" "V11838" "V11839" "V11840"
## [11841] "V11841" "V11842" "V11843" "V11844" "V11845" "V11846" "V11847" "V11848"
## [11849] "V11849" "V11850" "V11851" "V11852" "V11853" "V11854" "V11855" "V11856"
## [11857] "V11857" "V11858" "V11859" "V11860" "V11861" "V11862" "V11863" "V11864"
## [11865] "V11865" "V11866" "V11867" "V11868" "V11869" "V11870" "V11871" "V11872"
## [11873] "V11873" "V11874" "V11875" "V11876" "V11877" "V11878" "V11879" "V11880"
## [11881] "V11881" "V11882" "V11883" "V11884" "V11885" "V11886" "V11887" "V11888"
## [11889] "V11889" "V11890" "V11891" "V11892" "V11893" "V11894" "V11895" "V11896"
## [11897] "V11897" "V11898" "V11899" "V11900" "V11901" "V11902" "V11903" "V11904"
## [11905] "V11905" "V11906" "V11907" "V11908" "V11909" "V11910" "V11911" "V11912"
## [11913] "V11913" "V11914" "V11915" "V11916" "V11917" "V11918" "V11919" "V11920"
## [11921] "V11921" "V11922" "V11923" "V11924" "V11925" "V11926" "V11927" "V11928"
## [11929] "V11929" "V11930" "V11931" "V11932" "V11933" "V11934" "V11935" "V11936"
## [11937] "V11937" "V11938" "V11939" "V11940" "V11941" "V11942" "V11943" "V11944"
## [11945] "V11945" "V11946" "V11947" "V11948" "V11949" "V11950" "V11951" "V11952"
## [11953] "V11953" "V11954" "V11955" "V11956" "V11957" "V11958" "V11959" "V11960"
## [11961] "V11961" "V11962" "V11963" "V11964" "V11965" "V11966" "V11967" "V11968"
## [11969] "V11969" "V11970" "V11971" "V11972" "V11973" "V11974" "V11975" "V11976"
## [11977] "V11977" "V11978" "V11979" "V11980" "V11981" "V11982" "V11983" "V11984"
## [11985] "V11985" "V11986" "V11987" "V11988" "V11989" "V11990" "V11991" "V11992"
## [11993] "V11993" "V11994" "V11995" "V11996" "V11997" "V11998" "V11999" "V12000"
## 
## $class
## [1] "data.frame"
## 
## $row.names
##   [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18
##  [19]  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
##  [37]  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54
##  [55]  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72
##  [73]  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90
##  [91]  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200
npuntos <- nrow(imagenductilfragilfatigaxeydf)
base_bspline <- create.bspline.basis(c(1, npuntos), 150)
plot(base_bspline)

summary(base_bspline)
## 
## Basis object:
## 
##   Type:   bspline 
## 
##   Range:  1  to  200 
## 
##   Number of basis functions:  150
# Selección de lambda
gcv <- NULL
lambda_list <- seq(-2, 4, length.out = 30)
# estos son las potencias de 10 de variación del lambda
# inicia con valores muy cercanos a cero 10^-2 hasta 10^4
matrix_data <- as.matrix(imagenductilfragilfatigaxeydf)
for (lambda in lambda_list) {
  lambda <- 10**lambda
  param_lambda <- fdPar(base_bspline,
                        2,
                        lambda)
  fd_smoothductilfragilfatiga <- smooth.basis(argvals = seq(1,
                                          nrow(matrix_data)),
                            y = matrix_data,
                            fdParobj = param_lambda)
  gcv <- c(gcv, sum(fd_smoothductilfragilfatiga$gcv))
}
# Se esta tomando como criterio total la suma de las VC de todas las curvas
plotgcv <-
  data.frame(log_lambda = lambda_list,
             gcv = gcv) %>% ggplot(aes(x = log_lambda, y = gcv)) +
  geom_line(color = "purple", linetype = "dashed") +
  theme_light() +
  scale_x_continuous(limits = c(0, 4))
plotly::ggplotly(plotgcv)
selected_lamdba <- 10**lambda_list[which.min(gcv)]
print(selected_lamdba)
## [1] 0.02592944
print(log(selected_lamdba, 10))
## [1] -1.586207
# entrega el valor de lambda donde se minimiza la suma de VC y log de lambda

# Suavizamiento con el lambda elegido
param_lambda <- fdPar(base_bspline,
                      2,
                      lambda = selected_lamdba)

fd_smoothductilfragilfatiga <- smooth.basis(argvals = seq(1,
                                        nrow(matrix_data)),
                          y = matrix_data,
                          fdParobj = param_lambda)
plot(fd_smoothductilfragilfatiga, xlab= "Pixel", ylab = "Intensidad")

## [1] "done"
plot(fd_smoothductilfragilfatiga$fd[1], xlab= "Pixel", ylab = "Intensidad")

## [1] "done"
# una curva

# Comparación de curvas originales y suavizadas
fd_fittedductilfragilfatiga <- fitted(fd_smoothductilfragilfatiga)
plot(imagenductilfragilfatigaxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,1],col="2")

4.2 Estimación de PCA y scores de todas las graficas en conjunto con 150 harmonicos (aca variar el numero de armónicos)

Los porcentajes de varianza que recogen las diez primeras funciones principales son: 1.620517e+01 3.280065e+00 2.282422e+00 2.036705e+00 1.953267e+00 1.792524e+00 1.645079e+00 1.640718e+00 1.630027e+00 1.617558e+00. Hasta la funcion propia 82 se acumula el 90.10906% de la varianza.

# Estimación de valores propios, scores y funciones propias para las curvas combinadas de dúctil, frÔgil y fatiga
Curvas_Ductil_Fragil_Fatiga.pca<-pca.fd(fd_smoothductilfragilfatiga$fd,nharm=150,harmfdPar = fdPar(fd_smoothductilfragilfatiga$fd))
# Escoger armonicos en funcion del porcentaje de varianza
lambda_comb<-Curvas_Ductil_Fragil_Fatiga.pca$values
# Valores propios

v_comb<-Curvas_Ductil_Fragil_Fatiga.pca$harmonics
# funciones propias

plot(v_comb[1:3])

## [1] "done"
# Visualización primeras tres funciones principales

scores_comb<-Curvas_Ductil_Fragil_Fatiga.pca$scores
# scores

sum_val_prop <- sum(Curvas_Ductil_Fragil_Fatiga.pca$values)
# suma de valores propios

Val_prop_percent <- (Curvas_Ductil_Fragil_Fatiga.pca$values/sum_val_prop)*100;Val_prop_percent
##   [1] 1.620644e+01 3.280059e+00 2.282260e+00 2.036683e+00 1.953137e+00
##   [6] 1.792502e+00 1.644910e+00 1.640767e+00 1.630118e+00 1.617428e+00
##  [11] 1.489976e+00 1.438652e+00 1.410583e+00 1.364453e+00 1.337212e+00
##  [16] 1.315606e+00 1.265654e+00 1.236721e+00 1.202021e+00 1.170329e+00
##  [21] 1.146386e+00 1.105780e+00 1.089872e+00 1.075171e+00 1.051185e+00
##  [26] 1.048959e+00 1.013120e+00 9.665306e-01 9.644357e-01 9.626638e-01
##  [31] 9.294053e-01 9.125078e-01 9.044202e-01 8.908965e-01 8.799031e-01
##  [36] 8.590860e-01 8.329008e-01 8.220929e-01 8.033277e-01 7.887951e-01
##  [41] 7.808222e-01 7.642014e-01 7.599564e-01 7.554088e-01 7.445708e-01
##  [46] 7.255069e-01 7.125825e-01 6.917065e-01 6.805968e-01 6.714922e-01
##  [51] 6.673596e-01 6.583499e-01 6.503759e-01 6.324194e-01 6.269833e-01
##  [56] 6.095032e-01 6.029750e-01 5.903988e-01 5.887136e-01 5.744889e-01
##  [61] 5.679565e-01 5.635257e-01 5.581181e-01 5.339415e-01 5.230769e-01
##  [66] 5.064254e-01 5.038554e-01 4.938183e-01 4.907079e-01 4.817517e-01
##  [71] 4.682597e-01 4.556852e-01 4.505787e-01 4.468858e-01 4.329680e-01
##  [76] 4.292581e-01 4.162757e-01 3.992664e-01 3.932808e-01 3.841740e-01
##  [81] 3.803664e-01 3.772694e-01 3.619537e-01 3.541131e-01 3.456969e-01
##  [86] 3.410059e-01 3.266573e-01 3.228928e-01 3.161324e-01 3.049117e-01
##  [91] 2.916372e-01 2.895922e-01 2.846308e-01 2.754148e-01 2.694354e-01
##  [96] 2.581949e-01 2.511557e-01 2.420246e-01 2.369330e-01 2.299882e-01
## [101] 2.235730e-01 2.134765e-01 2.074576e-01 2.021717e-01 1.938217e-01
## [106] 1.818580e-01 1.784887e-01 1.711137e-01 1.652111e-01 1.605650e-01
## [111] 1.469526e-01 1.395333e-01 1.384630e-01 1.329312e-01 1.263923e-01
## [116] 1.219247e-01 1.182595e-01 1.098905e-01 1.078044e-01 1.015643e-01
## [121] 9.583792e-02 9.225233e-02 8.899882e-02 8.293576e-02 7.754296e-02
## [126] 7.507881e-02 7.105198e-02 6.703905e-02 6.383861e-02 5.857575e-02
## [131] 5.469912e-02 5.257265e-02 4.994933e-02 4.613382e-02 4.363242e-02
## [136] 4.171746e-02 3.953864e-02 3.767121e-02 3.731400e-02 3.399513e-02
## [141] 3.290323e-02 3.184443e-02 3.111074e-02 3.017683e-02 2.888971e-02
## [146] 2.786749e-02 2.646151e-02 2.614341e-02 8.407452e-04 7.602429e-04
# Porcentaje de la varianza de valores propios

4.3 Paso de las curvas a su representación en componentes principales con 150 harmonicos

Curvas_Ductil_Fragil_Fatiga.EnBasePCA <- reconsCurves(imagenductilfragilfatigaxey,Curvas_Ductil_Fragil_Fatiga.pca)
plot(Curvas_Ductil_Fragil_Fatiga.EnBasePCA, xlab= "Pixel", ylab = "Intensidad")

## [1] "done"
plot(Curvas_Ductil_Fragil_Fatiga.EnBasePCA[1], xlab= "Pixel", ylab = "Intensidad")

## [1] "done"
# una curva

# Comparación de curvas originales y suavizadas
x1 <- seq(1:200)
fd_fittedductilfragilfatiga <- eval.fd(x1,Curvas_Ductil_Fragil_Fatiga.EnBasePCA)
plot(imagenductilfragilfatigaxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,1],col="2")

plot(imagenductilfragilfatigaxey[,6000],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,6000],col="2")

plot(imagenductilfragilfatigaxey[,12000],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,12000],col="2")

4.4. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (familia binomial)

Corre en 2 minutos.

La fracción de acierto global es de 0.585, para imÔgenes dúctiles es de 0.818, para imÔgenes frÔgiles de 0.698 y para imÔgenes de fatiga 0.238.

Por fotos:

La fracción de acierto global es de 0.777.

Tres de tres dúctiles se adjudican a dúctil. Acierto 100%. Tres de tres frÔgiles se adjudican correctamente. Acierto 100%. Uno de tres de fatiga se adjudicó correcta. Acierto 33%.

(Dio igual que con base bspline cuando se usan todos los 150 harmonicos).

fd_fittedductilfragilfatiga_t <- t(fd_fittedductilfragilfatiga)
Curvas_Ent_PCA_Matriz <- rbind(fd_fittedductilfragilfatiga_t[1:2800,],fd_fittedductilfragilfatiga_t[4001:6800,],fd_fittedductilfragilfatiga_t[8001:10800,])

Curvas_Ens_PCA_Matriz <- rbind(fd_fittedductilfragilfatiga_t[2801:4000,],fd_fittedductilfragilfatiga_t[6801:8000,],fd_fittedductilfragilfatiga_t[10801:12000,])

# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frÔgil y categoría 3 es fatiga
CatEnt <- as.factor(c(rep(1,2800), rep(2,2800), rep(3,2800)))

# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frÔgil y categoría 3 es fatiga
CatEns <- as.factor(c(rep(1,1200), rep(2,1200), rep(3,1200)))

CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEntfdata <- fdata(Curvas_Ent_PCA_Matriz,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = binomial()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion

fd_smoothEnsfdata <- fdata(Curvas_Ens_PCA_Matriz,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista 
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo
##       p1
## CatEns   1   2   3
##      1 982  44 174
##      2 132 836 232
##      3 183 730 287
t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion
## [1] 0.5847222
t[1,1]/1200
## [1] 0.8183333
t[2,2]/1200
## [1] 0.6966667
t[3,3]/1200
## [1] 0.2391667
Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil
##      [,1] [,2] [,3]
## [1,]  394    0    6
## [2,]  299   17   84
## [3,]  289   27   84
# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frÔgil y en 3 fatiga

Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil
##      [,1] [,2] [,3]
## [1,]   60  236  104
## [2,]    2  395    3
## [3,]   70  205  125
# Clasificacion por fotos frÔgiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frÔgil y en 3 fatiga

Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga
##      [,1] [,2] [,3]
## [1,]   47  249  104
## [2,]  129  134  137
## [3,]    7  347   46
# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frÔgil y en 3 fatiga



# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenductilfragilfatigaxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")

# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenductilfragilfatigaxey[,2801],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")

4.5 Estimación de PCA y scores de todas las graficas en conjunto con 1 harmonicos (aca variar el numero de armónicos)

Los porcentajes de varianza que recogen las diez primeras funciones principales son: 1.620517e+01 3.280065e+00 2.282422e+00 2.036705e+00 1.953267e+00 1.792524e+00 1.645079e+00 1.640718e+00 1.630027e+00 1.617558e+00. Hasta la funcion propia 82 se acumula el 90.10906% de la varianza.

# Estimación de valores propios, scores y funciones propias para las curvas combinadas de dúctil, frÔgil y fatiga
Curvas_Ductil_Fragil_Fatiga.pca<-pca.fd(fd_smoothductilfragilfatiga$fd,nharm=1,harmfdPar = fdPar(fd_smoothductilfragilfatiga$fd))
# Escoger armonicos en funcion del porcentaje de varianza
lambda_comb<-Curvas_Ductil_Fragil_Fatiga.pca$values
# Valores propios

v_comb<-Curvas_Ductil_Fragil_Fatiga.pca$harmonics
# funciones propias

plot(v_comb[1:3])

## [1] "done"
# Visualización primeras tres funciones principales

scores_comb<-Curvas_Ductil_Fragil_Fatiga.pca$scores
# scores

sum_val_prop <- sum(Curvas_Ductil_Fragil_Fatiga.pca$values)
# suma de valores propios

Val_prop_percent <- (Curvas_Ductil_Fragil_Fatiga.pca$values/sum_val_prop)*100;Val_prop_percent
##   [1] 1.620644e+01 3.280059e+00 2.282260e+00 2.036683e+00 1.953137e+00
##   [6] 1.792502e+00 1.644910e+00 1.640767e+00 1.630118e+00 1.617428e+00
##  [11] 1.489976e+00 1.438652e+00 1.410583e+00 1.364453e+00 1.337212e+00
##  [16] 1.315606e+00 1.265654e+00 1.236721e+00 1.202021e+00 1.170329e+00
##  [21] 1.146386e+00 1.105780e+00 1.089872e+00 1.075171e+00 1.051185e+00
##  [26] 1.048959e+00 1.013120e+00 9.665306e-01 9.644357e-01 9.626638e-01
##  [31] 9.294053e-01 9.125078e-01 9.044202e-01 8.908965e-01 8.799031e-01
##  [36] 8.590860e-01 8.329008e-01 8.220929e-01 8.033277e-01 7.887951e-01
##  [41] 7.808222e-01 7.642014e-01 7.599564e-01 7.554088e-01 7.445708e-01
##  [46] 7.255069e-01 7.125825e-01 6.917065e-01 6.805968e-01 6.714922e-01
##  [51] 6.673596e-01 6.583499e-01 6.503759e-01 6.324194e-01 6.269833e-01
##  [56] 6.095032e-01 6.029750e-01 5.903988e-01 5.887136e-01 5.744889e-01
##  [61] 5.679565e-01 5.635257e-01 5.581181e-01 5.339415e-01 5.230769e-01
##  [66] 5.064254e-01 5.038554e-01 4.938183e-01 4.907079e-01 4.817517e-01
##  [71] 4.682597e-01 4.556852e-01 4.505787e-01 4.468858e-01 4.329680e-01
##  [76] 4.292581e-01 4.162757e-01 3.992664e-01 3.932808e-01 3.841740e-01
##  [81] 3.803664e-01 3.772694e-01 3.619537e-01 3.541131e-01 3.456969e-01
##  [86] 3.410059e-01 3.266573e-01 3.228928e-01 3.161324e-01 3.049117e-01
##  [91] 2.916372e-01 2.895922e-01 2.846308e-01 2.754148e-01 2.694354e-01
##  [96] 2.581949e-01 2.511557e-01 2.420246e-01 2.369330e-01 2.299882e-01
## [101] 2.235730e-01 2.134765e-01 2.074576e-01 2.021717e-01 1.938217e-01
## [106] 1.818580e-01 1.784887e-01 1.711137e-01 1.652111e-01 1.605650e-01
## [111] 1.469526e-01 1.395333e-01 1.384630e-01 1.329312e-01 1.263923e-01
## [116] 1.219247e-01 1.182595e-01 1.098905e-01 1.078044e-01 1.015643e-01
## [121] 9.583792e-02 9.225233e-02 8.899882e-02 8.293576e-02 7.754296e-02
## [126] 7.507881e-02 7.105198e-02 6.703905e-02 6.383861e-02 5.857575e-02
## [131] 5.469912e-02 5.257265e-02 4.994933e-02 4.613382e-02 4.363242e-02
## [136] 4.171746e-02 3.953864e-02 3.767121e-02 3.731400e-02 3.399513e-02
## [141] 3.290323e-02 3.184443e-02 3.111074e-02 3.017683e-02 2.888971e-02
## [146] 2.786749e-02 2.646151e-02 2.614341e-02 8.407452e-04 7.602429e-04
# Porcentaje de la varianza de valores propios

4.6 Paso de las curvas a su representación en componentes principales con 1 harmonicos

Curvas_Ductil_Fragil_Fatiga.EnBasePCA <- reconsCurves(imagenductilfragilfatigaxey,Curvas_Ductil_Fragil_Fatiga.pca)
plot(Curvas_Ductil_Fragil_Fatiga.EnBasePCA, xlab= "Pixel", ylab = "Intensidad")

## [1] "done"
plot(Curvas_Ductil_Fragil_Fatiga.EnBasePCA[1], xlab= "Pixel", ylab = "Intensidad")

## [1] "done"
# una curva

# Comparación de curvas originales y suavizadas
x1 <- seq(1:200)
fd_fittedductilfragilfatiga <- eval.fd(x1,Curvas_Ductil_Fragil_Fatiga.EnBasePCA)
plot(imagenductilfragilfatigaxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,1],col="2")

plot(imagenductilfragilfatigaxey[,6000],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,6000],col="2")

plot(imagenductilfragilfatigaxey[,12000],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,12000],col="2")

4.7. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (familia binomial)

Corre en 2 minutos.

La fracción de acierto global es de 0.6066667, para imÔgenes dúctiles es de 0.8741667, para imÔgenes frÔgiles de 0.705 y para imÔgenes de fatiga 0.2408333.

Por fotos:

La fracción de acierto global es de 0.777.

Tres de tres dúctiles se adjudican a dúctil. Acierto 100%. Tres de tres frÔgiles se adjudican correctamente. Acierto 100%. Uno de tres de fatiga se adjudicó correcta. Acierto 33%.

fd_fittedductilfragilfatiga_t <- t(fd_fittedductilfragilfatiga)
Curvas_Ent_PCA_Matriz <- rbind(fd_fittedductilfragilfatiga_t[1:2800,],fd_fittedductilfragilfatiga_t[4001:6800,],fd_fittedductilfragilfatiga_t[8001:10800,])

Curvas_Ens_PCA_Matriz <- rbind(fd_fittedductilfragilfatiga_t[2801:4000,],fd_fittedductilfragilfatiga_t[6801:8000,],fd_fittedductilfragilfatiga_t[10801:12000,])

# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frÔgil y categoría 3 es fatiga
CatEnt <- as.factor(c(rep(1,2800), rep(2,2800), rep(3,2800)))

# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frÔgil y categoría 3 es fatiga
CatEns <- as.factor(c(rep(1,1200), rep(2,1200), rep(3,1200)))

CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEntfdata <- fdata(Curvas_Ent_PCA_Matriz,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = binomial()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion

fd_smoothEnsfdata <- fdata(Curvas_Ens_PCA_Matriz,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista 
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo
##       p1
## CatEns    1    2    3
##      1 1049   32  119
##      2  131  846  223
##      3  177  734  289
t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion
## [1] 0.6066667
t[1,1]/1200
## [1] 0.8741667
t[2,2]/1200
## [1] 0.705
t[3,3]/1200
## [1] 0.2408333
Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil
##      [,1] [,2] [,3]
## [1,]  398    0    2
## [2,]  326   11   63
## [3,]  325   21   54
# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frÔgil y en 3 fatiga

Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil
##      [,1] [,2] [,3]
## [1,]   60  239  101
## [2,]    0  395    5
## [3,]   71  212  117
# Clasificacion por fotos frÔgiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frÔgil y en 3 fatiga

Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga
##      [,1] [,2] [,3]
## [1,]   59  275   66
## [2,]  115  106  179
## [3,]    3  353   44
# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frÔgil y en 3 fatiga



# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenductilfragilfatigaxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")

# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenductilfragilfatigaxey[,2801],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")

4.8 Estimación de PCA y scores de todas las graficas en conjunto con 2 harmonicos (aca variar el numero de armónicos)

Los porcentajes de varianza que recogen las diez primeras funciones principales son: 1.620517e+01 3.280065e+00 2.282422e+00 2.036705e+00 1.953267e+00 1.792524e+00 1.645079e+00 1.640718e+00 1.630027e+00 1.617558e+00. Hasta la funcion propia 82 se acumula el 90.10906% de la varianza.

# Estimación de valores propios, scores y funciones propias para las curvas combinadas de dúctil, frÔgil y fatiga
Curvas_Ductil_Fragil_Fatiga.pca<-pca.fd(fd_smoothductilfragilfatiga$fd,nharm=2,harmfdPar = fdPar(fd_smoothductilfragilfatiga$fd))
# Escoger armonicos en funcion del porcentaje de varianza
lambda_comb<-Curvas_Ductil_Fragil_Fatiga.pca$values
# Valores propios

v_comb<-Curvas_Ductil_Fragil_Fatiga.pca$harmonics
# funciones propias

plot(v_comb[1:2])

## [1] "done"
# Visualización primeras tres funciones principales

scores_comb<-Curvas_Ductil_Fragil_Fatiga.pca$scores
# scores

sum_val_prop <- sum(Curvas_Ductil_Fragil_Fatiga.pca$values)
# suma de valores propios

Val_prop_percent <- (Curvas_Ductil_Fragil_Fatiga.pca$values/sum_val_prop)*100;Val_prop_percent
##   [1] 1.620644e+01 3.280059e+00 2.282260e+00 2.036683e+00 1.953137e+00
##   [6] 1.792502e+00 1.644910e+00 1.640767e+00 1.630118e+00 1.617428e+00
##  [11] 1.489976e+00 1.438652e+00 1.410583e+00 1.364453e+00 1.337212e+00
##  [16] 1.315606e+00 1.265654e+00 1.236721e+00 1.202021e+00 1.170329e+00
##  [21] 1.146386e+00 1.105780e+00 1.089872e+00 1.075171e+00 1.051185e+00
##  [26] 1.048959e+00 1.013120e+00 9.665306e-01 9.644357e-01 9.626638e-01
##  [31] 9.294053e-01 9.125078e-01 9.044202e-01 8.908965e-01 8.799031e-01
##  [36] 8.590860e-01 8.329008e-01 8.220929e-01 8.033277e-01 7.887951e-01
##  [41] 7.808222e-01 7.642014e-01 7.599564e-01 7.554088e-01 7.445708e-01
##  [46] 7.255069e-01 7.125825e-01 6.917065e-01 6.805968e-01 6.714922e-01
##  [51] 6.673596e-01 6.583499e-01 6.503759e-01 6.324194e-01 6.269833e-01
##  [56] 6.095032e-01 6.029750e-01 5.903988e-01 5.887136e-01 5.744889e-01
##  [61] 5.679565e-01 5.635257e-01 5.581181e-01 5.339415e-01 5.230769e-01
##  [66] 5.064254e-01 5.038554e-01 4.938183e-01 4.907079e-01 4.817517e-01
##  [71] 4.682597e-01 4.556852e-01 4.505787e-01 4.468858e-01 4.329680e-01
##  [76] 4.292581e-01 4.162757e-01 3.992664e-01 3.932808e-01 3.841740e-01
##  [81] 3.803664e-01 3.772694e-01 3.619537e-01 3.541131e-01 3.456969e-01
##  [86] 3.410059e-01 3.266573e-01 3.228928e-01 3.161324e-01 3.049117e-01
##  [91] 2.916372e-01 2.895922e-01 2.846308e-01 2.754148e-01 2.694354e-01
##  [96] 2.581949e-01 2.511557e-01 2.420246e-01 2.369330e-01 2.299882e-01
## [101] 2.235730e-01 2.134765e-01 2.074576e-01 2.021717e-01 1.938217e-01
## [106] 1.818580e-01 1.784887e-01 1.711137e-01 1.652111e-01 1.605650e-01
## [111] 1.469526e-01 1.395333e-01 1.384630e-01 1.329312e-01 1.263923e-01
## [116] 1.219247e-01 1.182595e-01 1.098905e-01 1.078044e-01 1.015643e-01
## [121] 9.583792e-02 9.225233e-02 8.899882e-02 8.293576e-02 7.754296e-02
## [126] 7.507881e-02 7.105198e-02 6.703905e-02 6.383861e-02 5.857575e-02
## [131] 5.469912e-02 5.257265e-02 4.994933e-02 4.613382e-02 4.363242e-02
## [136] 4.171746e-02 3.953864e-02 3.767121e-02 3.731400e-02 3.399513e-02
## [141] 3.290323e-02 3.184443e-02 3.111074e-02 3.017683e-02 2.888971e-02
## [146] 2.786749e-02 2.646151e-02 2.614341e-02 8.407452e-04 7.602429e-04
# Porcentaje de la varianza de valores propios

4.9 Paso de las curvas a su representación en componentes principales con 2 harmonicos

Curvas_Ductil_Fragil_Fatiga.EnBasePCA <- reconsCurves(imagenductilfragilfatigaxey,Curvas_Ductil_Fragil_Fatiga.pca)
plot(Curvas_Ductil_Fragil_Fatiga.EnBasePCA, xlab= "Pixel", ylab = "Intensidad")

## [1] "done"
plot(Curvas_Ductil_Fragil_Fatiga.EnBasePCA[1], xlab= "Pixel", ylab = "Intensidad")

## [1] "done"
# una curva

# Comparación de curvas originales y suavizadas
x1 <- seq(1:200)
fd_fittedductilfragilfatiga <- eval.fd(x1,Curvas_Ductil_Fragil_Fatiga.EnBasePCA)
plot(imagenductilfragilfatigaxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,1],col="2")

plot(imagenductilfragilfatigaxey[,6000],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,6000],col="2")

plot(imagenductilfragilfatigaxey[,12000],type = "o",xlab="Pixel",ylab="Intensidad")
lines(fd_fittedductilfragilfatiga[,12000],col="2")

4.10. Crear el modelo GAM con los datos de entrenamiento y probarlo con los datos de ensayo (familia binomial)

Corre en 2 minutos.

La fracción de acierto global es de 0.6047222, para imÔgenes dúctiles es de 0.8608333, para imÔgenes frÔgiles de 0.7091667 y para imÔgenes de fatiga 0.2441667.

Por fotos:

La fracción de acierto global es de 0.777.

Tres de tres dúctiles se adjudican a dúctil. Acierto 100%. Tres de tres frÔgiles se adjudican correctamente. Acierto 100%. Uno de tres de fatiga se adjudicó correcta. Acierto 33%.

fd_fittedductilfragilfatiga_t <- t(fd_fittedductilfragilfatiga)
Curvas_Ent_PCA_Matriz <- rbind(fd_fittedductilfragilfatiga_t[1:2800,],fd_fittedductilfragilfatiga_t[4001:6800,],fd_fittedductilfragilfatiga_t[8001:10800,])

Curvas_Ens_PCA_Matriz <- rbind(fd_fittedductilfragilfatiga_t[2801:4000,],fd_fittedductilfragilfatiga_t[6801:8000,],fd_fittedductilfragilfatiga_t[10801:12000,])

# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frÔgil y categoría 3 es fatiga
CatEnt <- as.factor(c(rep(1,2800), rep(2,2800), rep(3,2800)))

# Crear el factor de categorias de tratamientos
# Categoria 1 es dúctil, categoría 2 es frÔgil y categoría 3 es fatiga
CatEns <- as.factor(c(rep(1,1200), rep(2,1200), rep(3,1200)))

CatEntDF<-data.frame(CatEnt) # se convierten en data frame las categorias de las curvas de entrenamiento
fd_smoothEntfdata <- fdata(Curvas_Ent_PCA_Matriz,1:200) # Se convierte en fdata los datos de entrenamiento fd
dat=list("df"=CatEntDF,"x"=fd_smoothEntfdata) # se crea la lista con las categorias de las curvas de entrenamiento y los datos funciones de entrenamiento
a1<-classif.gsam(CatEnt~x, data = dat, family = binomial()) # Se obtienen los parametros de GAM, dentro del objeto estan las probabilidades e clasificacion

fd_smoothEnsfdata <- fdata(Curvas_Ens_PCA_Matriz,1:200) # Se convierte en fdata los datos de ensayo fd
newdat<-list("x"=fd_smoothEnsfdata) # se asignan las curvas de ensayo como una lista 
p1<-predict(a1,newdat) # se hace la predicción de la categoria de las curvas de ensayo de acuerdo al modelo GLM, el objeto entregado es un factor
table(CatEns,p1) # genera tabla de contingencia donde se puede ver la coincidencia entre la categoria conocida y la predicha por el modelo
##       p1
## CatEns    1    2    3
##      1 1033   32  135
##      2  124  851  225
##      3  169  738  293
t <- table(CatEns,p1)
sum(p1==CatEns)/3600 # se determina la fracción de aciertos en la clasificacion
## [1] 0.6047222
t[1,1]/1200
## [1] 0.8608333
t[2,2]/1200
## [1] 0.7091667
t[3,3]/1200
## [1] 0.2441667
Fductil <- matrix(0,3,3)
Fductil[1,1] <- sum(p1[1:400]==1); Fductil[1,2] <- sum(p1[1:400]==2); Fductil[1,3] <- sum(p1[1:400]==3)
Fductil[2,1] <- sum(p1[401:800]==1); Fductil[2,2] <- sum(p1[401:800]==2); Fductil[2,3] <- sum(p1[401:800]==3)
Fductil[3,1] <- sum(p1[801:1200]==1); Fductil[3,2] <- sum(p1[801:1200]==2); Fductil[3,3] <- sum(p1[801:1200]==3)
Fductil
##      [,1] [,2] [,3]
## [1,]  399    0    1
## [2,]  332   11   57
## [3,]  302   21   77
# Clasificacion por fotos dúctiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frÔgil y en 3 fatiga

Ffragil <- matrix(0,3,3)
Ffragil[1,1] <- sum(p1[1201:1600]==1); Ffragil[1,2] <- sum(p1[1201:1600]==2); Ffragil[1,3] <- sum(p1[1201:1600]==3)
Ffragil[2,1] <- sum(p1[1601:2000]==1); Ffragil[2,2] <- sum(p1[1601:2000]==2); Ffragil[2,3] <- sum(p1[1601:2000]==3)
Ffragil[3,1] <- sum(p1[2001:2400]==1); Ffragil[3,2] <- sum(p1[2001:2400]==2); Ffragil[3,3] <- sum(p1[2001:2400]==3)
Ffragil
##      [,1] [,2] [,3]
## [1,]   59  240  101
## [2,]    0  395    5
## [3,]   65  216  119
# Clasificacion por fotos frÔgiles. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frÔgil y en 3 fatiga

Ffatiga <- matrix(0,3,3)
Ffatiga[1,1] <- sum(p1[2401:2800]==1); Ffatiga[1,2] <- sum(p1[2401:2800]==2); Ffatiga[1,3] <- sum(p1[2401:2800]==3)
Ffatiga[2,1] <- sum(p1[2801:3200]==1); Ffatiga[2,2] <- sum(p1[2801:3200]==2); Ffatiga[2,3] <- sum(p1[2801:3200]==3)
Ffatiga[3,1] <- sum(p1[3201:3600]==1); Ffatiga[3,2] <- sum(p1[3201:3600]==2); Ffatiga[3,3] <- sum(p1[3201:3600]==3)
Ffatiga
##      [,1] [,2] [,3]
## [1,]   63  275   62
## [2,]  101  111  188
## [3,]    5  352   43
# Clasificacion por fotos de fatiga. Cada fila es una fotos de ensayo y cada columna contador de clasificación en columna 1 dúctil, en 2 frÔgil y en 3 fatiga



# Comparación de curvas originales y suavizadas pasadas a fdata Ent
plot(imagenductilfragilfatigaxey[,1],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEnt[,1],col="2")
lines(fd_smoothEntfdata$data[1,],col="3")

# Comparación de curvas originales y suavizadas pasadas a fdata Ens
plot(imagenductilfragilfatigaxey[,2801],type = "o",xlab="Pixel",ylab="Intensidad")
#lines(fd_fittedEns[,1],col="2")
lines(fd_smoothEnsfdata$data[1,],col="3")